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class Solution:
def nearestPalindromic(self, n: str) -> str:
def getPalindromes(s: str) -> tuple:
num = int(s)
k = len(s)
palindromes = []
half = s[0:(k + 1) // 2]
reversedHalf = half[:k // 2][::-1]
candidate = int(half + reversedHalf)
if candidate < num:
palindromes.append(candidate)
else:
prevHalf = str(int(half) - 1)
reversedPrevHalf = prevHalf[:k // 2][::-1]
if k % 2 == 0 and int(prevHalf) == 0:
palindromes.append(9)
elif k % 2 == 0 and (int(prevHalf) + 1) % 10 == 0:
palindromes.append(int(prevHalf + '9' + reversedPrevHalf))
else:
palindromes.append(int(prevHalf + reversedPrevHalf))
if candidate > num:
palindromes.append(candidate)
else:
nextHalf = str(int(half) + 1)
reversedNextHalf = nextHalf[:k // 2][::-1]
palindromes.append(int(nextHalf + reversedNextHalf))
return palindromes
prevPalindrome, nextPalindrome = getPalindromes(n)
return str(prevPalindrome) if abs(prevPalindrome - int(n)) <= abs(nextPalindrome - int(n)) else str(nextPalindrome)
|
"""
Dana jest tablica T[N][N] (reprezentująca szachownicę) wypełniona liczbami naturalnymi.
Proszę napisać funkcję która ustawia na szachownicy dwie wieże, tak aby suma liczb na „szachowanych”
przez wieże polach była największa. Do funkcji należy przekazać tablicę, funkcja powinna zwrócić
położenie wież. Uwaga: zakładamy, że wieża szachuje cały wiersz i kolumnę z wyłączeniem
pola na którym stoi.
"""
def column_summary(array, row, col):
result = 0
for i in range(len(array)):
result += array[i][col]
result -= array[row][col]
return result
def row_summary(array, row, col):
result = 0
for i in range(len(array)):
result += array[row][i]
result -= array[row][col]
return result
def two_rooks(array):
max_result = 0
tower_1 = tower_2 = 0
for i in range(len(array)):
for j in range(len(array)):
for k in range(i, len(array)):
for m in range(len(array)):
if i != k and j != m:
result = column_summary(array, i, j) + column_summary(array, k, m) \
+ row_summary(array, i, j) + row_summary(array, k, m)
result -= array[i][m]
result -= array[k][j]
if result > max_result:
tower_1 = (i, j)
tower_2 = (k, m)
max_result = result
return tower_1[0], tower_1[1], tower_2[0], tower_2[1]
array = [[1, 1, 2, 3],
[-1, 3, -1, 4],
[4, 1, 5, 4],
[5, 0, 3, 6]]
print(two_rooks(array))
|
def is_prime(num: int) -> bool:
prime_nums_list: list = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]
if num in prime_nums_list:
return True
elif list(str(num))[-1] in ['2', '5']:
return False
else:
for n in range(2, num):
if (num % n) == 0:
return False
return True
print(is_prime(int(input())))
|
# create a simple tree data structure with python
# First of all: a class implemented to present tree node
class TreeNode:
def __init__(self, val):
self.val = val
self.left = None
self.right = None
class Tree:
def __init__(self):
self.root = None
self.size = 0
def addnode(self, new):
if self.root == None:
self.root = new
self.size += 1
else:
self.helper(self.root, new)
def helper(self, innode, new):
if innode == None:
innode = new
self.size += 1
elif new.val < innode.val:
if innode.left == None:
innode.left = new
self.size += 1
else:
self.helper(innode.left, new)
elif new.val > innode.val:
if innode.right == None:
innode.right = new
self.size += 1
else:
self.helper(innode.right, new)
else:
print("can't have duplicate node!")
def Inorder(root):
if root != None:
Inorder(root.left)
print(root.val, end=" ")
Inorder(root.right)
elderwood = Tree()
n1 = TreeNode(3)
n2 = TreeNode(5)
n3 = TreeNode(22)
n4 = TreeNode(1)
n5 = TreeNode(0)
n6 = TreeNode(-7)
n7 = TreeNode(8)
n8 = TreeNode(100)
n9 = TreeNode(-50)
elderwood.addnode(n1)
elderwood.addnode(n2)
elderwood.addnode(n3)
elderwood.addnode(n4)
elderwood.addnode(n5)
elderwood.addnode(n6)
elderwood.addnode(n7)
elderwood.addnode(n8)
elderwood.addnode(n9)
Inorder(elderwood.root)
print("\nTotal tree nodes: %d" % elderwood.size)
|
class Solution:
def lengthOfLongestSubstringKDistinct(self, s: str, k: int) -> int:
if k == 0: return 0
maxLen = 0
d = {}
i, j = 0, 0
while j < len(s):
d[s[j]] = d.get(s[j], 0) + 1
if len(d) <= k:
tempMax = 0
for key, val in d.items():
tempMax += val
maxLen = max(maxLen, tempMax)
j += 1
else:
d[s[i]] -= 1
d[s[j]] -= 1
if d[s[i]] == 0:
d.pop(s[i])
i += 1
return maxLen
|
# -*- coding: utf-8 -*-
"""Module for flask views.
Only the home page view is defined in this scope. All other views are defined
in nested modules for partitioning.
"""
|
leap = Runtime.start("leap","LeapMotion")
leap.addLeapDataListener(python)
def onLeapData(data):
print (data.rightHand.index)
leap.startTracking()
|
language="java"
print("Checking if else conditions")
if language=='Python':
print(Python)
elif language=="java":
print("java")
else:
print("no match")
print("\nChecking Boolean Conditions")
user='Admin'
logged_in=False
if user=='Admin' and logged_in:
print("ADMIN PAGE")
else:
print("Bad Creds")
if not logged_in:
print("Please Log In")
else:
print("Welcome")
print("\nWorking with Object Identity")
a=[1,2,3]
b=[1,2,3]
print(id(a))
print(id(b))
print(a==b)
print(a is b)
b=a
print(a is b) # or
print("same as")
print(id(a)==id(b))
print("\nChecking False Conditions")
condition= '34234'
if(condition):
print("Evaluated to true")
else:
print("Evaluated to false")
|
podcast_title = 'Chaos im Radio'
hello_text = r'''<p>Der ChaosTreff Potsdam macht mit beim <a href="http://frrapo.de/player/">Freien Radio Potsdam</a>.</p>
<p>Hier könnt ihr Sendungen nachhören.</p>'''
website = 'https://radio.ccc-p.org'
media_base_url = website + '/files'
cover_image = website + '/cover.jpg'
small_cover_image = website + '/cover_250.jpg'
category = 'Technology'
feed_url = website + '/feed.xml'
language = 'de-DE'
author_name = 'ChaosTreff Potsdam'
copyright = '2019 ' + author_name
|
"""Implement an algorithm that takes a BST and transform it into a
circular double linked list. The transformation must be done in place.
BST:
4
2 6
1 5 10
CDLL:
______________________
/ \
1 <> 2 <> 4 <> 5 <> 6 <> 10
\______________________/
(1 is connected to 10)
"""
class TreeNode(object):
def __init__(self, value):
self.value = value
self.left = None
self.right = None
def make_double_linked_list(tree):
"""Return a circle double linked list from a binary search tree.
The space complexity of this algorithm is O(N) in the worst case,
and O(log(N)) in the case of a balanced tree.
The time complexity is O(N).
:param tree: TreeNode
:return: TreeNode
"""
tail = None
previous = None
def _traverse_inorder(node):
if node is None:
return
_traverse_inorder(node.left)
nonlocal previous
if previous:
node.left = previous
previous.right = node
previous = node
nonlocal tail
if tail is None:
tail = node
_traverse_inorder(node.right)
_traverse_inorder(tree)
print("At the end of the process, tail points to:", tail.value)
print("At the end of the process, previous points to:", previous.value)
tail.left = previous
previous.right = tail
return previous
if __name__ == "__main__":
"""
Constructed binary tree is
10
/ \
6 15
/ \ \
4 7 20
\
5
"""
root = TreeNode(10)
root.left = TreeNode(6)
root.right = TreeNode(15)
root.right.right = TreeNode(20)
root.left.left = TreeNode(4)
root.left.left.right = TreeNode(5)
root.left.right = TreeNode(7)
head = make_double_linked_list(root)
ptr = head.right
c = 10
while c > 0:
print(ptr.value)
ptr = ptr.right
c -= 1
ll = make_double_linked_list(TreeNode(10))
assert ll.left == ll and ll.right == ll
|
class Adder:
a = 0
b = 0
def add(self):
return self.a + self.b
def __init__(self,a,b):
self.a = a;
self.b = b;
a = int(input('Enter first number: '))
b = int(input('Enter second number: '))
x = Adder(a,b)
print(x.add()) |
# O programa calculará o preço da passagem sabendo a distância. Para viagens de até 200km o preço por km
# é de R$ 0,50. Para viagens acima de 200km o preço do km rodado é R$ 0,45
k = float(input('Olá! Qual a distância da sua rota em km? '))
preço = k * 0.5 if k <= 200 else k * 0.45
print('O preço da passagem será de: R$ {:.2f}'.format(preço))
|
def char_sum(s: str):
sum = 0
for char in s:
sum += ord(char)
return sum
# Time complexity: O(M+N)
# Space complexity: O(1)
def check_permutation(s1: str, s2: str):
sum1 = char_sum(s1)
sum2 = char_sum(s2)
return sum1 == sum2
print(check_permutation("same", "same"))
print(check_permutation("same", "smae"))
print(check_permutation("same", "not same"))
|
# -*- coding: utf-8 -*-
#
# Copyright (C) 2021 CERN.
#
# Invenio-App-RDM is free software; you can redistribute it and/or modify
# it under the terms of the MIT License; see LICENSE file for more details.
"""Test that all export formats are working."""
def test_export_formats(client, running_app, record):
"""Test that all expected export formats are working."""
# Expected export formats:
formats = [
"json",
"csl",
"datacite-json",
"datacite-xml",
"dublincore",
]
for f in formats:
res = client.get(f"/records/{record.id}/export/{f}")
assert res.status_code == 200
|
class APIError(RuntimeError):
def __init__(self, message):
self.message = message
def __str__(self):
return "%s" % (self.message)
def __repr__(self):
return self.__str__()
|
kumas, inus, ookamis = 10, 4, 16
if (kumas > inus) and (kumas > ookamis):
print(ookamis)
elif (inus > kumas) and (inus > ookamis):
print(kumas)
elif (ookamis > kumas) and (ookamis > inus):
print(inus)
|
L = [92,456,34,7234,24,7,623,5,35]
maxSoFar = L[0]
for i in range(len(L)):
if L[i] > maxSoFar:
maxSoFar = L[i]
print(maxSoFar)
|
class Empleado:
cantidad_empleados = 0
tasa_incremento = 1.03
def __init__(self, nombre, apellido, email, sueldo):
self.nombre = nombre
self.apellido = apellido
self.email = email
self.sueldo = sueldo
def get_full_name(self):
return '{} {}'.format(self.nombre, self.apellido)
def get_new_salary(self):
self.sueldo = int(self.sueldo * self.tasa_incremento)
emp1 = Empleado('Jane', 'Willis', '[email protected]', 2500)
emp2 = Empleado('Jack', 'Ryan', '[email protected]', 5500)
print(emp1.get_full_name())
print(emp2.get_full_name())
print(Empleado.get_full_name(emp1))
print(emp1.__dict__)
print(emp1.sueldo)
emp1.get_new_salary()
print(emp1.sueldo)
print(Empleado.tasa_incremento)
print(emp1.tasa_incremento)
print(emp2.tasa_incremento)
emp2.tasa_incremento = 2.1
print(emp2.__dict__)
print(Empleado.__dict__)
Empleado.tasa_incremento = 1.5
print(Empleado.tasa_incremento)
print(emp1.tasa_incremento)
print(emp2.tasa_incremento)
emp1.tasa_incremento = 1.5
print(emp1.__dict__)
emp2.foo = 3
print(emp2.__dict__)
|
class A:
def long_unique_identifier(self): pass
def foo(x):
x.long_unique_identifier()
# <ref> |
# -*- coding: utf-8 -*-
def parametrized(dec):
def layer(*args, **kwargs):
def repl(f):
return dec(f, *args, **kwargs)
return repl
return layer
@parametrized
def dependency(module, *_deps):
module.deps = _deps
return module
@parametrized
def source(module, _source):
module.source = _source
return module
@parametrized
def version(module, _ver):
module.version = _ver
return module
@dependency()
@source('unknown')
@version('latest')
class Module(object):
def __init__(self, composer):
self.composer = composer
def __repr__(self):
return '%-13s %-6s (%s)' % (
self.name(),
self.version,
self.source)
def build(self):
pass
def expose(self):
return []
def name(self):
return self.__class__.__name__.lower()
|
print("Enter The Number n")
n = int(input())
if (n%2)!=0:
print("Weird")
elif (n%2)==0:
if n in range(2,5):
print("Not Weird")
elif n in range(6,21):
print("Weird")
elif n > 20:
print("Not Weird")
|
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
""" Unit test cases for cellular environment and algorithms """
__author__ = 'Ari Saha ([email protected]), Mingyang Liu([email protected])'
|
model = dict(
type='TSN2D',
backbone=dict(
type='ResNet',
pretrained='modelzoo://resnet50',
nsegments=8,
depth=50,
out_indices=(3,),
tsm=True,
bn_eval=False,
partial_bn=False),
spatial_temporal_module=dict(
type='SimpleSpatialModule',
spatial_type='avg',
spatial_size=7),
segmental_consensus=dict(
type='SimpleConsensus',
consensus_type='avg'),
cls_head=dict(
type='ClsHead',
with_avg_pool=False,
temporal_feature_size=1,
spatial_feature_size=1,
dropout_ratio=0.5,
in_channels=2048,
num_classes=174))
train_cfg = None
test_cfg = None
# dataset settings
dataset_type = 'RawFramesDataset'
data_root = ''
data_root_val = ''
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
data = dict(
videos_per_gpu=8,
workers_per_gpu=8,
train=dict(
type=dataset_type,
ann_file='data/sthv1/train_videofolder.txt',
img_prefix=data_root,
img_norm_cfg=img_norm_cfg,
num_segments=8,
new_length=1,
new_step=1,
random_shift=True,
modality='RGB',
image_tmpl='{:05d}.jpg',
img_scale=256,
input_size=224,
flip_ratio=0.5,
resize_keep_ratio=True,
resize_crop=True,
color_jitter=True,
color_space_aug=True,
oversample=None,
max_distort=1,
test_mode=False),
val=dict(
type=dataset_type,
ann_file='data/sthv1/val_videofolder.txt',
img_prefix=data_root_val,
img_norm_cfg=img_norm_cfg,
num_segments=8,
new_length=1,
new_step=1,
random_shift=False,
modality='RGB',
image_tmpl='{:05d}.jpg',
img_scale=256,
input_size=224,
flip_ratio=0,
resize_keep_ratio=True,
oversample=None,
test_mode=False),
test=dict(
type=dataset_type,
ann_file='data/sthv1/val_videofolder.txt',
img_prefix=data_root_val,
img_norm_cfg=img_norm_cfg,
num_segments=16,
new_length=1,
new_step=1,
random_shift=False,
modality='RGB',
image_tmpl='{:05d}.jpg',
img_scale=256,
input_size=256,
flip_ratio=0,
resize_keep_ratio=True,
oversample="three_crop",
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True)
optimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[75, 125])
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
# yapf:disable
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 150
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
|
"""
Xilinx primitive tokens
"""
ASYNC_PORTS = "async_ports"
CLK_PORTS = "clk_ports"
CLOCK_BUFFERS = "clock_buffers"
COMBINATIONAL_CELLS = "combinational_cells"
COMPLEX_SEQUENTIAL_CELLS = "complex_sequential_cells"
DESCRIPTION = "description"
FF_CELLS = "ff_cells"
MISC_CELLS = "misc_cells"
NAME = "name"
POWER_GROUND_CELLS = "power_ground_cells"
PRIMITIVE_LIBRARY_NAME = "primitive_library_name"
SYNC_PORTS = "sync_ports"
VENDOR = "vendor"
|
#AUTHOR: Pornpimol Kaewphing
#Python3 Concept: Twosum in Python
#GITHUB: https://github.com/gympohnpimol
def twoSum(self, nums: List[int], target: int) -> List[int]:
for i in range(len(nums)):
for j in range(i + 1, len(nums)):
if nums[i] + nums[j] == target:
return [i,j]
else:
pass
|
def combine_toxic_classes(df):
"""""""""
Reconfigures the Jigsaw Toxic Comment dataset from a
multi-label classification problem to a
binary classification problem predicting if a text is
toxic (class=1) or non-toxic (class=0).
Input:
- df: A pandas DataFrame with columns:
- 'id'
- 'comment_text'
- 'toxic'
- 'severe_toxic'
- 'obscene'
- 'threat'
- 'insult'
- 'identity_hate'
Output:
- df: A modified pandas DataFrame with columns:
- 'comment_text' containing strings of text.
- 'isToxic' binary target variable containing 0's and 1's.
"""""""""
# Create a binary classification label for 'isToxic'
# and drop miscellaneous labels.
df['isToxic'] = (df['toxic'] == 1)
drop_cols = ['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
df.drop(columns=drop_cols, inplace=True)
df.replace(to_replace={'isToxic': {True: 1, False: 0}}, inplace=True)
# Cast column values to save memory
df['isToxic'] = df['isToxic'].astype('int8')
return df
def undersample_majority(df, percent_conserve):
"""""""""
Undersamples the majority class of the Jigsaw Toxic Comment dataset
('isToxic'==0) by conserving a given percent of the majority class.
Inputs:
- df: A pandas DataFrame with columns:
- 'comment_text' containing strings of text.
- 'isToxic' binary target variable containing 0's and 1's.
- percent_conserve: Float representing fraction of
majority class (clean_texts) to conserve
Outputs:
- downsampled_df: A new pandas DataFrame that has been shuffled
and has had its majority class downsampled.
"""""""""
# Get rows of clean and toxic texts
clean_texts = df[df['isToxic'] == 0]
toxic_texts = df[df['isToxic'] == 1]
# Randomly sample from the majority class and construct a new DataFrame
# consisting of the majority class (clean_texts) + the minority classes (toxic_texts)
to_conserve = clean_texts.sample(frac=percent_conserve, random_state=42)
downsampled_df = to_conserve.append(toxic_texts, ignore_index=True)
return downsampled_df.sample(frac=1, random_state=42).reset_index(drop=True)
def analyze_dist(df):
"""""""""
Analyzes the class distribution of a pandas DataFrame.
Input:
- df: a pandas DataFrame containing text whose toxicity is denoted
by the 'isToxic' binary indicator column.
Output:
- Prints class distribution (toxic or non-toxic) statistics of df.
"""""""""
print('Total rows: ', df.shape[0])
print('Clean texts: ', df.shape[0] - df['isToxic'].sum())
print('Toxic texts: ', df['isToxic'].sum())
print('Toxic texts make up ', ((df['isToxic'].sum() / df.shape[0]) * 100).round(2), 'percent of our total data')
return
def get_relevant_words(text, to_conserve):
"""""""""
Takes a string of text and returns the first N words in that text.
Input:
- text: String of text
- to_conserve: Integer representing number of text's words to conserve
Output:
- String containing first (to_conserve) words of text.
"""""""""
# Select the first N words in the text
word_list = text.split()[:to_conserve]
# Build up a string containing words in word_list
new_string = ' '.join(word for word in word_list)
return new_string
def augment_sentence(sentence, aug, num_threads):
"""""""""
Constructs a new sentence via text augmentation.
Input:
- sentence: A string of text
- aug: An augmentation object defined by the nlpaug library
- num_threads: Integer controlling the number of threads to use if
augmenting text via CPU
Output:
- A string of text that been augmented
"""""""""
return aug.augment(sentence, num_thread=num_threads)
def augment_text(df, aug, num_threads, num_times):
"""""""""
Takes a pandas DataFrame and augments its text data.
Input:
- df: A pandas DataFrame containing the columns:
- 'comment_text' containing strings of text to augment.
- 'isToxic' binary target variable containing 0's and 1's.
- aug: Augmentation object defined by the nlpaug library.
- num_threads: Integer controlling number of threads to use if augmenting
text via CPU
- num_times: Integer representing the number of times to augment text.
Output:
- df: Copy of the same pandas DataFrame with augmented data
appended to it and with rows randomly shuffled.
"""""""""
# Get rows of data to augment
to_augment = df[df['isToxic'] == 1]
to_augmentX = to_augment['comment_text']
to_augmentY = np.ones(len(to_augmentX.index) * num_times, dtype=np.int8)
# Build up dictionary containing augmented data
aug_dict = {'comment_text': [], 'isToxic': to_augmentY}
for i in tqdm(range(num_times)):
augX = [augment_sentence(x, aug, num_threads) for x in to_augmentX]
aug_dict['comment_text'].extend(augX)
# Build DataFrame containing augmented data
aug_df = pd.DataFrame.from_dict(aug_dict)
return df.append(aug_df, ignore_index=True).sample(frac=1, random_state=42)
|
list1 = [1, 2, 3]
list2 = ["One", "Two"]
print("list1: ", list1)
print("list2: ", list2)
print("\n")
list12 = list1 + list2
print("list1 + list2: ", list12)
list2x3 = list2 * 3
print("list2 * 3: ", list2x3)
hasThree = "Three" in list2
print("'Three' in list2? ", hasThree) |
# -*- coding: utf-8 -*-
"""
@Time : 2020/11/26 15:38
@Author : PyDee
@File : __init__.py.py
@description :
"""
|
def print_in_blocks(li, bp):
dli = []
temp_list = []
for i in range(len(li)):
temp_list.append(li[i])
if i != 0 and (i+1)%bp == 0:
dli.append(temp_list)
temp_list = []
cols = bp
max_col_len = []
for _ in range(cols):
max_col_len.append(0)
for i in range(len(max_col_len)):
for r in dli:
if max_col_len[i] < len(r[i]):
max_col_len[i] = len(r[i])
print(end='+-')
for i in range(cols):
print('-' * max_col_len[i], end='-+-')
print(end='\b \n')
for i in dli:
print(end='| ')
for j in range(cols):
print(i[j].ljust(max_col_len[j]), end=' | ')
print()
print(end='+-')
for i in range(cols):
print('-' * max_col_len[i], end='-+-')
print(end='\b \n') |
# These should reflect //ci/prebuilt/BUILD declared targets. This a map from
# target in //ci/prebuilt/BUILD to the underlying build recipe in
# ci/build_container/build_recipes.
TARGET_RECIPES = {
"ares": "cares",
"backward": "backward",
"event": "libevent",
"event_pthreads": "libevent",
# TODO(htuch): This shouldn't be a build recipe, it's a tooling dependency
# that is external to Bazel.
"gcovr": "gcovr",
"googletest": "googletest",
"tcmalloc_and_profiler": "gperftools",
"http_parser": "http-parser",
"lightstep": "lightstep",
"nghttp2": "nghttp2",
"protobuf": "protobuf",
"protoc": "protobuf",
"rapidjson": "rapidjson",
"spdlog": "spdlog",
"ssl": "boringssl",
"tclap": "tclap",
}
|
colocacao = ('São Paulo','Coritiba','Corinthians','Atlétigo-MG','Ceará','Avaí','Cuiabá','Bragantino','Juventude','Flamengo','Atlético-GO','Santos','Fluminense','Palmeiras','Fortaleza','América-MG','Botafogo','Internacional',
'Goiás','Athletico-PR')
print('=-'*20)
print(f'Lista de times do Brasileirão 2022: {colocacao}')
print('=-'*20)
print(f'Os 5 primeiros são: {colocacao[0:5]}')
print('=-'*20)
print(f'Os 4 últimos são: {colocacao[16:]}')
print('=-'*20)
print(f'Times em ordem alfabética: {sorted(colocacao)}')
print('=-'*20)
print(f'O Flamengo está na {colocacao.index("Flamengo")+1}ª posição')
print('=-'*20)
|
# Python3 program to solve Rat in a Maze
# problem using backracking
# Maze size
N = 4
# A utility function to print solution matrix sol
def printSolution( sol ):
for i in sol:
for j in i:
print(str(j) + " ", end ="")
print("")
# A utility function to check if x, y is valid
# index for N * N Maze
def isSafe( maze, x, y ):
if x >= 0 and x < N and y >= 0 and y < N and maze[x][y] == 1:
return True
return False
def solveMaze( maze ):
# Creating a 4 * 4 2-D list
sol = [ [ 0 for j in range(4) ] for i in range(4) ]
if solveMazeUtil(maze, 0, 0, sol) == False:
print("Solution doesn't exist");
return False
printSolution(sol)
return True
# A recursive utility function to solve Maze problem
def solveMazeUtil(maze, x, y, sol):
# if (x, y is goal) return True
if x == N - 1 and y == N - 1 and maze[x][y]== 1:
sol[x][y] = 1
return True
# Check if maze[x][y] is valid
if isSafe(maze, x, y) == True:
# Check if the current block is already part of solution path.
if sol[x][y] == 1:
return False
# mark x, y as part of solution path
sol[x][y] = 1
#force rat to go to the right way
if solveMazeUtil(maze, x + 1, y, sol):
return True
if solveMazeUtil(maze, x, y + 1, sol):
return True
if solveMazeUtil(maze, x - 1, y, sol):
return True
if solveMazeUtil(maze, x, y - 1, sol):
return True
sol[x][y] = 0
return False
# Driver program to test above function
if __name__ == "__main__":
# Initialising the maze
maze = [ [1, 0, 0, 0],
[1, 1, 0, 1],
[0, 1, 0, 0],
[1, 1, 1, 1] ]
solveMaze(maze)
# This code is contributed by Shiv Shankar
# Also explain more by Sahachan Tippimwong to Submit the work on time |
# We use this to create easily readable errors for when debugging elastic beanstalk deployment configurations
class DynamicParameter(Exception): pass
#NOTE: integer values need to be strings
def get_base_eb_configuration():
return [
# Instance launch configuration details
{
'Namespace': 'aws:autoscaling:launchconfiguration',
'OptionName': 'InstanceType',
'Value': DynamicParameter("InstanceType")
},{
'Namespace': 'aws:autoscaling:launchconfiguration',
'OptionName': 'IamInstanceProfile',
'Value': DynamicParameter("IamInstanceProfile")
},{
'Namespace': 'aws:autoscaling:launchconfiguration',
'OptionName': 'EC2KeyName',
'Value': DynamicParameter("EC2KeyName")
},
# open up the ssh port for debugging - adds to the security group
{
'Namespace': 'aws:autoscaling:launchconfiguration',
'OptionName': 'SSHSourceRestriction',
'Value': 'tcp,22,22,0.0.0.0/0'
},
# cloudwatch alarms
{
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'BreachDuration',
'Value': '1'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'EvaluationPeriods',
'Value': '1'
},
# environment variables
{
'Namespace': 'aws:cloudformation:template:parameter',
'OptionName': 'EnvironmentVariables',
'Value': DynamicParameter("EnvironmentVariables"),
},
# },{ # could not get this to play well, so we modify it after the environment creates it.
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'SecurityGroups',
# 'Value': AutogeneratedParameter("SecurityGroups")
# },
{
'Namespace': 'aws:cloudformation:template:parameter',
'OptionName': 'InstancePort',
'Value': '80'
},
# deployment network details
# {
# 'Namespace': 'aws:ec2:vpc',
# 'OptionName': 'VPCId',
# 'Value': 'vpc-c6e16da2'
# },
# {
# 'Namespace': 'aws:ec2:vpc',
# 'OptionName': 'ELBSubnets',
# 'Value': 'subnet-10718a66,subnet-ea9599c1,subnet-8018a9bd,subnet-bf1f02e6'
# },
# {
# 'Namespace': 'aws:ec2:vpc',
# 'OptionName': 'Subnets',
# 'Value': 'subnet-10718a66,subnet-ea9599c1,subnet-8018a9bd,subnet-bf1f02e6'
# },
# static network details
# { # todo: not in a vpc?
# 'Namespace': 'aws:ec2:vpc',
# 'OptionName': 'AssociatePublicIpAddress',
# 'Value': 'true'
# },
{
'Namespace': 'aws:ec2:vpc',
'OptionName': 'ELBScheme',
'Value': 'public'
}, {
'Namespace': 'aws:elasticbeanstalk:application',
'OptionName': 'Application Healthcheck URL',
'Value': ''
},
# autoscaling settings
{
'Namespace': 'aws:autoscaling:asg',
'OptionName': 'Availability Zones',
'Value': 'Any'
}, {
'Namespace': 'aws:autoscaling:asg',
'OptionName': 'Cooldown',
'Value': '360'
}, {
'Namespace': 'aws:autoscaling:asg',
'OptionName': 'Custom Availability Zones',
'Value': ''
}, {
'Namespace': 'aws:autoscaling:asg',
'OptionName': 'MaxSize',
'Value': '2'
}, {
'Namespace': 'aws:autoscaling:asg',
'OptionName': 'MinSize',
'Value': '1'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'LowerBreachScaleIncrement',
'Value': '-1'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'LowerThreshold',
'Value': '20'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'MeasureName',
'Value': 'CPUUtilization'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'Period',
'Value': '1'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'Statistic',
'Value': 'Maximum'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'Unit',
'Value': 'Percent'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'UpperBreachScaleIncrement',
'Value': '1'
}, {
'Namespace': 'aws:autoscaling:trigger',
'OptionName': 'UpperThreshold',
'Value': '85'
}, {
'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
'OptionName': 'MaxBatchSize',
'Value': '1'
}, {
'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
'OptionName': 'MinInstancesInService',
'Value': '1'
},
# {
# 'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
# 'OptionName': 'PauseTime',
# },
{
'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
'OptionName': 'RollingUpdateEnabled',
'Value': 'true'
}, {
'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
'OptionName': 'RollingUpdateType',
'Value': 'Health'
}, {
'Namespace': 'aws:autoscaling:updatepolicy:rollingupdate',
'OptionName': 'Timeout',
'Value': 'PT30M'
},
# Logging settings
{
'Namespace': 'aws:elasticbeanstalk:cloudwatch:logs',
'OptionName': 'DeleteOnTerminate',
'Value': 'false'
}, {
'Namespace': 'aws:elasticbeanstalk:cloudwatch:logs',
'OptionName': 'RetentionInDays',
'Value': '7'
}, {
'Namespace': 'aws:elasticbeanstalk:cloudwatch:logs',
'OptionName': 'StreamLogs',
'Value': 'false'
},
# miscellaneous EB configuration
{
'Namespace': 'aws:elasticbeanstalk:command',
'OptionName': 'BatchSize',
'Value': '30'
}, {
'Namespace': 'aws:elasticbeanstalk:command',
'OptionName': 'BatchSizeType',
'Value': 'Percentage'
}, {
'Namespace': 'aws:elasticbeanstalk:command',
'OptionName': 'DeploymentPolicy',
'Value': 'Rolling'
}, {
'Namespace': 'aws:elasticbeanstalk:command',
'OptionName': 'IgnoreHealthCheck',
'Value': 'true'
}, { # Time at which a timeout occurs after deploying the environment - I think.
'Namespace': 'aws:elasticbeanstalk:command',
'OptionName': 'Timeout',
'Value': '300'
}, {
'Namespace': 'aws:elasticbeanstalk:control',
'OptionName': 'DefaultSSHPort',
'Value': '22'
}, {'Namespace': 'aws:elasticbeanstalk:control',
'OptionName': 'LaunchTimeout',
'Value': '0'
}, {
'Namespace': 'aws:elasticbeanstalk:control',
'OptionName': 'LaunchType',
'Value': 'Migration'
}, {
'Namespace': 'aws:elasticbeanstalk:control',
'OptionName': 'RollbackLaunchOnFailure',
'Value': 'false'
},
# Python environment configuration
{
'Namespace': 'aws:elasticbeanstalk:container:python',
'OptionName': 'NumProcesses',
'Value': '2'
}, {
'Namespace': 'aws:elasticbeanstalk:container:python',
'OptionName': 'NumThreads',
'Value': '20'
}, {
'Namespace': 'aws:elasticbeanstalk:container:python',
'OptionName': 'StaticFiles',
'Value': '/static/=frontend/static/'
}, {
'Namespace': 'aws:elasticbeanstalk:container:python',
'OptionName': 'WSGIPath',
'Value': 'wsgi.py'
}, {
'Namespace': 'aws:elasticbeanstalk:container:python:staticfiles',
'OptionName': '/static/',
'Value': 'frontend/static/'
},
# Elastic Beanstalk system Notifications
{ # These settings generate the SNS instance for sending these emails.
'Namespace': 'aws:elasticbeanstalk:sns:topics',
'OptionName': 'Notification Endpoint',
'Value': DynamicParameter('Notification Endpoint')
}, {
'Namespace': 'aws:elasticbeanstalk:sns:topics',
'OptionName': 'Notification Protocol',
'Value': 'email'
},
# Health check/Reporting details
{
'Namespace': 'aws:elasticbeanstalk:healthreporting:system',
'OptionName': 'ConfigDocument',
'Value': '{"Version":1,"CloudWatchMetrics":{"Instance":{"CPUIrq":null,"LoadAverage5min":null,"ApplicationRequests5xx":null,"ApplicationRequests4xx":null,"CPUUser":null,"LoadAverage1min":null,"ApplicationLatencyP50":null,"CPUIdle":null,"InstanceHealth":null,"ApplicationLatencyP95":null,"ApplicationLatencyP85":null,"RootFilesystemUtil":null,"ApplicationLatencyP90":null,"CPUSystem":null,"ApplicationLatencyP75":null,"CPUSoftirq":null,"ApplicationLatencyP10":null,"ApplicationLatencyP99":null,"ApplicationRequestsTotal":null,"ApplicationLatencyP99.9":null,"ApplicationRequests3xx":null,"ApplicationRequests2xx":null,"CPUIowait":null,"CPUNice":null},"Environment":{"InstancesSevere":null,"InstancesDegraded":null,"ApplicationRequests5xx":null,"ApplicationRequests4xx":null,"ApplicationLatencyP50":null,"ApplicationLatencyP95":null,"ApplicationLatencyP85":null,"InstancesUnknown":null,"ApplicationLatencyP90":null,"InstancesInfo":null,"InstancesPending":null,"ApplicationLatencyP75":null,"ApplicationLatencyP10":null,"ApplicationLatencyP99":null,"ApplicationRequestsTotal":null,"InstancesNoData":null,"ApplicationLatencyP99.9":null,"ApplicationRequests3xx":null,"ApplicationRequests2xx":null,"InstancesOk":null,"InstancesWarning":null}}}'
}, {
'Namespace': 'aws:elasticbeanstalk:healthreporting:system',
'OptionName': 'HealthCheckSuccessThreshold',
'Value': 'Ok'
}, {
'Namespace': 'aws:elasticbeanstalk:healthreporting:system',
'OptionName': 'SystemType',
'Value': 'enhanced'
}, {
'Namespace': 'aws:elasticbeanstalk:hostmanager',
'OptionName': 'LogPublicationControl',
'Value': 'false'
}, {
'Namespace': 'aws:elasticbeanstalk:managedactions',
'OptionName': 'ManagedActionsEnabled',
'Value': 'false'
}, # {
# 'Namespace': 'aws:elasticbeanstalk:managedactions',
# 'OptionName': 'PreferredStartTime'
# },
{
'Namespace': 'aws:elasticbeanstalk:managedactions:platformupdate',
'OptionName': 'InstanceRefreshEnabled',
'Value': 'false'
}, # {
# 'Namespace': 'aws:elasticbeanstalk:managedactions:platformupdate',
# 'OptionName': 'UpdateLevel'
# },
{
'Namespace': 'aws:elasticbeanstalk:monitoring',
'OptionName': 'Automatically Terminate Unhealthy Instances',
'Value': 'true'
}, {
'Namespace': 'aws:elb:healthcheck',
'OptionName': 'HealthyThreshold',
'Value': '3'
}, {
'Namespace': 'aws:elb:healthcheck',
'OptionName': 'Interval',
'Value': '10'
}, {
'Namespace': 'aws:elb:healthcheck',
'OptionName': 'Target',
'Value': 'TCP:80'
}, {
'Namespace': 'aws:elb:healthcheck',
'OptionName': 'Timeout',
'Value': '5'
}, {
'Namespace': 'aws:elb:healthcheck',
'OptionName': 'UnhealthyThreshold',
'Value': '5'
},
# Storage configuration. We use the default, which is 8gb gp2.
# {
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'BlockDeviceMappings',
# }, {
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'MonitoringInterval',
# }, {
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'RootVolumeIOPS',
# }, {
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'RootVolumeSize',
# },{
# 'Namespace': 'aws:autoscaling:launchconfiguration',
# 'OptionName': 'RootVolumeType',
# },
##
## Elastic Load Balancer configuration
##
{
'Namespace': 'aws:elasticbeanstalk:environment',
'OptionName': 'EnvironmentType',
'Value': 'LoadBalanced'
}, { # there are 2 ELBs, ELB classic and ELBv2. We use classic.
'Namespace': 'aws:elasticbeanstalk:environment',
'OptionName': 'LoadBalancerType',
'Value': 'classic'
}, {
'Namespace': 'aws:elasticbeanstalk:environment',
'OptionName': 'ServiceRole',
'Value': DynamicParameter("ServiceRole")
},# {
# 'Namespace': 'aws:elasticbeanstalk:environment',
# 'OptionName': 'ExternalExtensionsS3Bucket'
# }, {
# 'Namespace': 'aws:elasticbeanstalk:environment',
# 'OptionName': 'ExternalExtensionsS3Key'
# },{ # probably don't override this one, use the one it autogenerates and then modify
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'SecurityGroups',
# 'Value': 'sg-********'
# },{
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'InstancePort',
# 'Value': '80'
# }, {
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'InstanceProtocol',
# 'Value': 'HTTP'
# }, {
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'ListenerEnabled',
# 'Value': 'true'
# }, {
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'ListenerProtocol',
# 'Value': 'HTTP'
# }, {
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'PolicyNames',
# }, {
# 'Namespace': 'aws:elb:listener:80',
# 'OptionName': 'SSLCertificateId',
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'CrossZone',
# 'Value': 'true'
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'LoadBalancerHTTPPort',
# 'Value': '80'
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'LoadBalancerHTTPSPort',
# 'Value': 'OFF'
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'LoadBalancerPortProtocol',
# 'Value': 'HTTP'
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'LoadBalancerSSLPortProtocol',
# 'Value': 'HTTPS'
# }, {
# 'Namespace': 'aws:elb:loadbalancer',
# 'OptionName': 'SSLCertificateId',
# }, {
# 'Namespace': 'aws:elb:policies',
# 'OptionName': 'ConnectionDrainingEnabled',
# 'Value': 'true'
# }, {
# 'Namespace': 'aws:elb:policies',
# 'OptionName': 'ConnectionDrainingTimeout',
# 'Value': '20'
# }, {
# 'Namespace': 'aws:elb:policies',
# 'OptionName': 'ConnectionSettingIdleTimeout',
# 'Value': '60'
# },
]
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
class Student(object):
def __init__(self, name, score):
# 实例的变量名如果以__开头，就变成了一个私有变量（private），只有内部可以访问，外部不能访问
self.__name = name
self.__score = score
def get_grade(self):
if self.__score >= 90:
return 'A'
elif self.__score >= 60:
return 'B'
else:
return 'C'
# 数据封装
def get_score(self):
return '%s: %s' % (self.__name, self.__score)
def get_name(self):
return self.__name
def get_score(self):
return self.__score
def set_score(self, score):
if 0 <= score <= 100:
self.__score = score
else:
raise ValueError('bad score')
Adam = Student('Adam', 100)
# print(Adam.name, Adam.score)
# print_score(Adam)
Adam.get_score()
lisa = Student('Lisa', 89)
bart = Student('Bart', 59)
print(Adam.get_score(), Adam.get_grade())
print(lisa.get_score(), lisa.get_grade())
print(bart.get_score(), bart.get_grade())
Adam.set_score(90)
print(Adam.get_score())
|
'''
Context:
String compression
using counts
of repeated characters
Definitions:
Objective:
Assumptions:
Only lower and upper case letters present
Constraints:
Inputs:
string value
Algorithm flow:
Compress the string
if string empty: return emotty
if length of compressed string >= original string
return original string
else
return compressed string
Example(s):
aabcccccaaa => a2b1c5a3
Possible Solutions
character_count = 1
current_character = set to first character in string
compressed_string = ''
for the length of string
pick a character at current index
compare character with current_character
if same
increment character_count by 1
if not
append current_character and character_count to character_count
set character_count to 1
set current_character = character
Walk through
string length = 10
current_character = a
character_count = 3
compressed_string = a2b1c5a3
'''
def compress(string_input):
string_length = len(string_input)
compressed_string = ''
count_consecutive = 0
for i in range(string_length):
count_consecutive = count_consecutive + 1
if i + 1 >= string_length or string_input[i] != string_input[i + 1]:
compressed_string = compressed_string + string_input[i] + str(count_consecutive)
count_consecutive = 0
if len(compressed_string) >= string_length: return string_input
else: return compressed_string
print(compress('') == '') #true
print(compress('aabcccccaaa') == 'a2b1c5a3') #true
print(compress('abcdef') == 'abcdef') #true compressed same as original
'''
Performance
P = length of original string
K = number of consecutive character sequences
Time = O(P + K^2)
K^2 because string concatenation is O(N^2)
For each character sequence
we copy the compressed version and the current character sequence compression
into a new compressed string
Why is concatenation O(N^2)?
X = length of current string
N = number of strings
1st iteration = 1X copy
2nd iteration = 2X copy
3rd iteration = 3X copy
Nth iteration = NX copy
O(1X + 2X + 3X ... NX) => O(N^2)
1 + 2 + ... N = N(N + 1)/2 = O(N^2 + N) => O(N^2)
Space = O(2P) => O(P)
Compressed string might be twice as long
'''
|
class Solution(object):
def multiply(self, num1, num2):
"""
:type num1: str
:type num2: str
:rtype: str
"""
num1 = num1[: : -1]
num2 = num2[: : -1]
multi = [0 for i in range(len(num1) + len(num2))]
for i in range(len(num1)):
for j in range(len(num2)):
multi[i + j] += int(num1[i]) * int(num2[j])
carry = 0
for i in range(len(multi)):
carry += multi[i]
multi[i] = str(carry % 10)
carry /= 10
while carry:
multi.append(carry % 10)
carry /= 10
while len(multi) > 1 and '0' == multi[-1]:
del multi[-1]
return "".join(multi)[: : -1] |
# coding=utf-8
# Licensed Materials - Property of IBM
# Copyright IBM Corp. 2015,2017
"""
SPL primitive operators that call a Python function or
callable class are created by decorators provided :py:mod:`streamsx.spl.spl`
Once created the operators become part of a toolkit and may be used
like any other SPL operator.
"""
|
def slow_fib(n: int) -> int:
if n < 1:
return 0
if n == 1:
return 1
return slow_fib(n-1) + slow_fib(n-2)
|
def word_frequency(list):
words = {}
for word in list:
if word in words:
words[word] += 1
else:
words[word] = 1
return words
frequency_counter = word_frequency(['hey', 'hi', 'more', 'hey', 'hi'])
print(frequency_counter)
|
'''
Task
Given an integer, , and space-separated integers as input, create a tuple, , of those integers. Then compute and print the result of
.
Note: hash() is one of the functions in the __builtins__ module, so it need not be imported.
Input Format
The first line contains an integer,
, denoting the number of elements in the tuple.
The second line contains space-separated integers describing the elements in tuple
.
Output Format
Print the result of
.
Sample Input 0
2
1 2
Sample Output 0
3713081631934410656
'''
if __name__ == '__main__':
n = int(input())
integer_list = map(int, input().split())
print(hash(tuple(integer_list))) |
frase = str(input('Escreva uma frase: ')).strip().lower()
numA = frase.count('a')
pos1A = frase.find('a') + 1
posFA = frase.rfind('a') + 1
print('A frase digitada possui {} letras A.'.format(numA))
print('A primeira ocorrência da letra A esta na posição {}'.format(pos1A))
print('A última ocorrência da letra A esta na posição {}'.format(posFA)) |
# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# https://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
def inject_create_tags(event_name, class_attributes, **kwargs):
"""This injects a custom create_tags method onto the ec2 service resource
This is needed because the resource model is not able to express
creating multiple tag resources based on the fact you can apply a set
of tags to multiple ec2 resources.
"""
class_attributes['create_tags'] = create_tags
def create_tags(self, **kwargs):
# Call the client method
self.meta.client.create_tags(**kwargs)
resources = kwargs.get('Resources', [])
tags = kwargs.get('Tags', [])
tag_resources = []
# Generate all of the tag resources that just were created with the
# preceding client call.
for resource in resources:
for tag in tags:
# Add each tag from the tag set for each resource to the list
# that is returned by the method.
tag_resource = self.Tag(resource, tag['Key'], tag['Value'])
tag_resources.append(tag_resource)
return tag_resources
|
# GENERATED VERSION FILE
# TIME: Fri Mar 20 02:18:57 2020
__version__ = '1.1.0+58a3f02'
short_version = '1.1.0'
|
bruin = set(["Boxtel","Best","Beukenlaan","Helmond 't Hout","Helmond","Helmond Brouwhuis","Deurne"])
groen = set(["Boxtel","Best","Beukenlaan","Geldrop","Heeze","Weert"])
print(bruin.intersection(groen))
print(bruin.difference(groen))
print(bruin.union(groen))
|
#Exemplo 2
n1 = float(input('Digite sua primeira nota: '))
n2 = float(input('Digite sua segunda nota: '))
media = (n1 + n2)/ 2
if media >= 5:
print('Muito bem, sua media é de {:.1f} e está ótima!'.format(media))#{:.1f}formatação para uma casa decimal
else:
print('Sua média é de {:.1f}, você precisa estudar mais!'.format(media))
print('Boa sorte nos estudos!')
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def reverseList(self, head: ListNode) -> ListNode:
if not head:
return head
prevHead = head
while prevHead.next:
curr = prevHead.next
# move curr to the head
prevHead.next = curr.next
curr.next = head
head = curr
return head
# Recursion
class Solution:
def reverseList(self, head: ListNode) -> ListNode:
if not head or not head.next:
return head
left = self.reverseList(head.next)
# Place head node at the end
head.next.next = head
head.next = None
return left
|
# -*- coding: utf-8 -*-
X = int(input())
Y = int(input())
start, end = min(X, Y), max(X, Y)
firstDivisible = start if (start % 13 == 0) else start + (13 - (start % 13))
answer = sum(range(start, end + 1)) - sum(range(firstDivisible, end + 1, 13))
print(answer) |
"""Top-level package for Webex Bot."""
__author__ = """Finbarr Brady"""
__version__ = '0.2.5'
|
# variables
notasV = 0
soma = 0
# while there are not 2 grades between [0,10], so the loop continue
while notasV < 2:
# receive float
nota = float(input())
# if nota is >= 0 and nota <= 10
if (nota >= 0) and (nota <= 10):
notasV = notasV + 1
soma = soma + nota
# if it is not true
else:
print('nota invalida')
# media
if notasV == 2:
soma = soma / 2
print('media = {:.2f}'.format(soma))
|
# Error codes due to an invalid request
INVALID_REQUEST = 400
INVALID_ALGORITHM = 401
DOCUMENT_NOT_FOUND = 404
|
# -*- coding: utf-8 -*-
"""This module contains all the LUA code that needs to be on the device
to perform whats needed. They will be uploaded if they doesn't exist"""
# Copyright (C) 2015-2019 Peter Magnusson <[email protected]>
# pylint: disable=C0301
# flake8: noqa
LUA_FUNCTIONS = ['recv_block', 'recv_name', 'recv', 'shafile', 'send_block', 'send_file', 'send']
DOWNLOAD_FILE = "file.open('{filename}') print(file.seek('end', 0)) file.seek('set', {bytes_read}) uart.write(0, file.read({chunk_size}))file.close()"
PRINT_FILE = "file.open('{filename}') print('---{filename}---') print(file.read()) file.close() print('---')"
INFO_GROUP = "for key,value in pairs(node.info('{group}')) do k=tostring(key) print(k .. string.rep(' ', 20 - #k), tostring(value)) end"
LIST_FILES = 'for key,value in pairs(file.list()) do print(key,value) end'
# NUL = \000, ACK = \006
RECV_LUA = \
r"""
function recv()
local on,w,ack,nack=uart.on,uart.write,'\6','\21'
local fd
local function recv_block(d)
local t,l = d:byte(1,2)
if t ~= 1 then w(0, nack); fd:close(); return on('data') end
if l >= 0 then fd:write(d:sub(3, l+2)); end
if l == 0 then fd:close(); w(0, ack); return on('data') else w(0, ack) end
end
local function recv_name(d) d = d:gsub('%z.*', '') d:sub(1,-2) file.remove(d) fd=file.open(d, 'w') on('data', 130, recv_block, 0) w(0, ack) end
on('data', '\0', recv_name, 0)
w(0, 'C')
end
function shafile(f) print(crypto.toHex(crypto.fhash('sha1', f))) end
""" # noqa: E122
SEND_LUA = \
r"""
function send(f) uart.on('data', 1, function (data)
local on,w=uart.on,uart.write
local fd
local function send_block(d) l = string.len(d) w(0, '\001' .. string.char(l) .. d .. string.rep('\0', 128 - l)) return l end
local function send_file(f)
local s, p
fd=file.open(f) s=fd:seek('end', 0) p=0
on('data', 1, function(data)
if data == '\006' and p<s then
fd:seek('set',p) p=p+send_block(fd:read(128))
else
send_block('') fd:close() on('data') print('interrupted')
end
end, 0)
w(0, f .. '\000')
end
uart.on('data') if data == 'C' then send_file(f) else print('transfer interrupted') end end, 0)
end
"""
UART_SETUP = 'uart.setup(0,{baud},8,0,1,1)'
REMOVE_ALL_FILES = r"""
for key,value in pairs(file.list()) do file.remove(key) end
"""
|
# https://en.wikipedia.org/wiki/Trifid_cipher
def __encryptPart(messagePart, character2Number):
one, two, three = "", "", ""
tmp = []
for character in messagePart:
tmp.append(character2Number[character])
for each in tmp:
one += each[0]
two += each[1]
three += each[2]
return one + two + three
def __decryptPart(messagePart, character2Number):
tmp, thisPart = "", ""
result = []
for character in messagePart:
thisPart += character2Number[character]
for digit in thisPart:
tmp += digit
if len(tmp) == len(messagePart):
result.append(tmp)
tmp = ""
return result[0], result[1], result[2]
def __prepare(message, alphabet):
# Validate message and alphabet, set to upper and remove spaces
alphabet = alphabet.replace(" ", "").upper()
message = message.replace(" ", "").upper()
# Check length and characters
if len(alphabet) != 27:
raise KeyError("Length of alphabet has to be 27.")
for each in message:
if each not in alphabet:
raise ValueError("Each message character has to be included in alphabet!")
# Generate dictionares
numbers = (
"111",
"112",
"113",
"121",
"122",
"123",
"131",
"132",
"133",
"211",
"212",
"213",
"221",
"222",
"223",
"231",
"232",
"233",
"311",
"312",
"313",
"321",
"322",
"323",
"331",
"332",
"333",
)
character2Number = {}
number2Character = {}
for letter, number in zip(alphabet, numbers):
character2Number[letter] = number
number2Character[number] = letter
return message, alphabet, character2Number, number2Character
def encryptMessage(message, alphabet="ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5):
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
encrypted, encrypted_numeric = "", ""
for i in range(0, len(message) + 1, period):
encrypted_numeric += __encryptPart(message[i : i + period], character2Number)
for i in range(0, len(encrypted_numeric), 3):
encrypted += number2Character[encrypted_numeric[i : i + 3]]
return encrypted
def decryptMessage(message, alphabet="ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5):
message, alphabet, character2Number, number2Character = __prepare(message, alphabet)
decrypted_numeric = []
decrypted = ""
for i in range(0, len(message) + 1, period):
a, b, c = __decryptPart(message[i : i + period], character2Number)
for j in range(0, len(a)):
decrypted_numeric.append(a[j] + b[j] + c[j])
for each in decrypted_numeric:
decrypted += number2Character[each]
return decrypted
if __name__ == "__main__":
msg = "DEFEND THE EAST WALL OF THE CASTLE."
encrypted = encryptMessage(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
print("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted))
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
'model_alias',
]
# Records of model name to import class
model_alias = {
# ---------------------------------
# -------------- ASR --------------
# ---------------------------------
"deepspeech2offline": ["paddlespeech.s2t.models.ds2:DeepSpeech2Model"],
"deepspeech2online":
["paddlespeech.s2t.models.ds2:DeepSpeech2Model"],
"conformer": ["paddlespeech.s2t.models.u2:U2Model"],
"conformer_online": ["paddlespeech.s2t.models.u2:U2Model"],
"transformer": ["paddlespeech.s2t.models.u2:U2Model"],
"wenetspeech": ["paddlespeech.s2t.models.u2:U2Model"],
# ---------------------------------
# -------------- CLS --------------
# ---------------------------------
"panns_cnn6": ["paddlespeech.cls.models.panns:CNN6"],
"panns_cnn10": ["paddlespeech.cls.models.panns:CNN10"],
"panns_cnn14": ["paddlespeech.cls.models.panns:CNN14"],
# ---------------------------------
# -------------- ST ---------------
# ---------------------------------
"fat_st": ["paddlespeech.s2t.models.u2_st:U2STModel"],
# ---------------------------------
# -------------- TEXT -------------
# ---------------------------------
"ernie_linear_p7": [
"paddlespeech.text.models:ErnieLinear",
"paddlenlp.transformers:ErnieTokenizer"
],
"ernie_linear_p3": [
"paddlespeech.text.models:ErnieLinear",
"paddlenlp.transformers:ErnieTokenizer"
],
# ---------------------------------
# -------------- TTS --------------
# ---------------------------------
# acoustic model
"speedyspeech": ["paddlespeech.t2s.models.speedyspeech:SpeedySpeech"],
"speedyspeech_inference":
["paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference"],
"fastspeech2": ["paddlespeech.t2s.models.fastspeech2:FastSpeech2"],
"fastspeech2_inference":
["paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference"],
"tacotron2": ["paddlespeech.t2s.models.tacotron2:Tacotron2"],
"tacotron2_inference":
["paddlespeech.t2s.models.tacotron2:Tacotron2Inference"],
# voc
"pwgan": ["paddlespeech.t2s.models.parallel_wavegan:PWGGenerator"],
"pwgan_inference":
["paddlespeech.t2s.models.parallel_wavegan:PWGInference"],
"mb_melgan": ["paddlespeech.t2s.models.melgan:MelGANGenerator"],
"mb_melgan_inference": ["paddlespeech.t2s.models.melgan:MelGANInference"],
"style_melgan": ["paddlespeech.t2s.models.melgan:StyleMelGANGenerator"],
"style_melgan_inference":
["paddlespeech.t2s.models.melgan:StyleMelGANInference"],
"hifigan": ["paddlespeech.t2s.models.hifigan:HiFiGANGenerator"],
"hifigan_inference": ["paddlespeech.t2s.models.hifigan:HiFiGANInference"],
"wavernn": ["paddlespeech.t2s.models.wavernn:WaveRNN"],
"wavernn_inference": ["paddlespeech.t2s.models.wavernn:WaveRNNInference"],
# ---------------------------------
# ------------ Vector -------------
# ---------------------------------
"ecapatdnn": ["paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn"],
}
|
"""
The roseguarden project
Copyright (C) 2018-2020 Marcus Drobisch,
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <http://www.gnu.org/licenses/>.
"""
__authors__ = ["Marcus Drobisch"]
__contact__ = "[email protected]"
__credits__ = []
__license__ = "GPLv3"
class Action(object):
"""Base class that each action for every workspace have to inherit from.
The class define methods that all action must implement by the plugin
"""
disable = False
def __init__(self, app, uri=None):
if uri is None:
self.uri = self.__class__.__name__
else:
self.uri = uri
def handle(self, action, user, workspace, actionManager):
""" Action handler method
"""
raise NotImplementedError
@staticmethod
def generate(**kwargs):
""" Action generator method
"""
raise NotImplementedError
|
# Find minimum number without using conditional statement or ternary operator
def main():
a = 4
b = 3
print((a > b) * a + (a < b) * b)
if __name__ == '__main__':
main()
|
#!/usr/bin/env python3
"""project: Block Letters, created: 2022-01-13, author: seraph★776"""
def letter_S():
"""This function prints uppercase "S" in block letters."""
for row in range(7):
for col in range(5):
if row in [0, 3, 6] and col in [1, 2, 3] or (
col in [0] and row in [1, 2] or (col in [4] and row in [4, 5])):
print('*', end='')
else:
print(end=' ')
print()
print()
def letter_E():
"""This function prints uppercase "E" in block letters."""
for row in range(7):
for col in range(5):
if row in [0, 3, 6] or col in [0]:
print('*', end='')
else:
print(end=' ')
print()
print()
def letter_R():
"""This function prints uppercase "R" in block letters."""
for row in range(7):
for col in range(5):
if col in [0] or col in [4] and row not in [0, 3] or (row in [0, 3] and col in [1, 2, 3]):
print('*', end='')
else:
print(end=' ')
print()
print()
def letter_A():
"""This function prints uppercase "A" in block letters."""
for row in range(7):
for col in range(5):
if (col in [0, 4] and row not in [0]) or row in [0, 3] and col in range(1, 4):
print('*', end='')
else:
print(end=' ')
print()
print()
def letter_P():
"""This function prints uppercase "P" in block letters."""
for row in range(7):
for col in range(5):
if col in [0] or (col in [4] and row not in [0, 3, 4, 5, 6]) or (row in [0, 3] and col in [1, 2, 3]):
print('*', end='')
else:
print(end=' ')
print()
print()
def letter_H():
"""This function prints uppercase "H" in block letters."""
for row in range(7):
for col in range(5):
if col in [0, 4] or (row in [3] and col in [1, 2, 3]):
print('*', end='')
else:
print(end=' ')
print()
print()
def number_7():
"""This function prints the integer "7" in block letters."""
for row in range(7):
for col in range(5):
if row in [0] and col in range(5) or (col in [4] and row in range(7)):
print('*', end='')
else:
print(end=' ')
print()
print()
def number_6():
"""This function prints the integer "6" in block letters."""
for row in range(7):
for col in range(5):
if (col in [0] or col in [4] and row in range(3, 7)) or row in [0, 3, 6] and col in range(1, 5):
print('*', end='')
else:
print(end=' ')
print()
print()
def seraph76():
"""This function displays 'seraph776' in block letters in vertical format."""
letter_S()
letter_E()
letter_R()
letter_A()
letter_P()
letter_H()
number_7()
number_7()
number_6()
def display_blockletters():
seraph76()
|
class Author:
def __init__(self, name, familyname=None):
self.name = name
self.familyname = familyname
def __repr__(self):
return u'{0}'.format(self.name)
authors = {'1': Author('Test Author'), '2': Author('Testy McTesterson')}
print(list(authors.values()))
print(u'Found {0} unique authors: {1}'.format(len(authors), list(authors.values())))
author2 = Author('Test Author 2')
name = author2.familyname or author2.name
print('Name: {0}'.format(name))
print(author2.familyname)
|
"""
categories: Types,bytearray
description: Array slice assignment with unsupported RHS
cause: Unknown
workaround: Unknown
"""
b = bytearray(4)
b[0:1] = [1, 2]
print(b)
|
"""
Given an integer, write an algorithm to convert it to hexadecimal. For negative
integer, two’s complement method is used.
Note:
All letters in hexadecimal (a-f) must be in lowercase.
The hexadecimal string must not contain extra leading 0s. If the number is
zero, it is represented by a single zero character '0'; otherwise, the first
character in the hexadecimal string will not be the zero character.
The given number is guaranteed to fit within the range of a 32-bit signed
integer.
You must not use any method provided by the library which converts/formats
the number to hex directly.
Example 1:
Input:
26
Output:
"1a"
Example 2:
Input:
-1
Output:
"ffffffff"
"""
class Solution(object):
hexChar = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'a', 'b', 'c', 'd', 'e', 'f']
def toHex(self, num):
"""
:type num: int
:rtype: str
"""
if num == 0:
return '0'
while num < 0:
num += 2 ** 32
if num > 2 ** 32 - 1:
num = 2 ** 32 - 1
soln = ''
while num > 0:
soln += self.hexChar[num % 16]
num //= 16
return soln[::-1]
a = Solution()
print(a.toHex(26) == "1a")
print(a.toHex(-1) == "ffffffff")
|
'''
'''
def main():
info('Fill Pipette 1')
close(description='Outer Pipette 1')
sleep(1)
if analysis_type=='blank':
info('not filling cocktail pipette')
else:
info('filling cocktail pipette')
open(description='Inner Pipette 1')
sleep(15)
close(description='Inner Pipette 1')
sleep(1) |
# Exercício Python 65: Crie um programa que leia vários números inteiros pelo teclado. No final da execução, mostre a média entre todos os valores e qual foi o maior e o menor valores lidos. O programa deve perguntar ao usuário se ele quer ou não continuar a digitar valores.
N = int(input('Numero: '))
lista = []
count = 0
S = 0
while S != 1:
N = int(input('Numero: '))
lista.append(N)
count += 1
S = int(input('Deseja continuar? \n[0]Sim\n[1]Não\n'))
print(f'Foi colocado {count} itens na lista.')
x = (sum(lista)/count)
print(f'A media de todos eles é: {x}')
print(max(lista))
print(min(lista)) |
_base_ = [
'../_base_/models/regproxy/regproxy-l16.py',
'../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py',
'../_base_/schedules/adamw+cr+lr_6e-5+wd_0.01+iter_80k.py'
]
model = dict(
backbone=dict(
img_size=(768, 768),
out_indices=[5, 23]),
test_cfg=dict(
mode='slide',
crop_size=(768, 768),
stride=(512, 512)))
|
# Copy this file to config.py and fill the blanks
QCLOUD_APP_ID = ''
QCLOUD_SECRET_ID = ''
QCLOUD_SECRET_KEY = ''
QCLOUD_BUCKET = ''
QCLOUD_REGION = 'sh'
|
class MICOptimizer(object):
"""A simple wrapper class for learning rate scheduling"""
def __init__(self, optimizer):
self.optimizer = optimizer
self.step_num = 0
self.lr = 3e-5
def zero_grad(self):
self.optimizer.zero_grad()
def step(self):
self._update_lr()
self.optimizer.step()
def _update_lr(self):
self.step_num += 1
# Initial learning rate was 3 × 10−5 and dropped to 10−5
# and 3 × 10−6 when loss plateaued, at 200k and 375k iterations, respectively
if self.step_num == 200000:
self.lr = 1e-5
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
elif self.step_num == 375000:
self.lr = 3e-6
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
def clip_gradient(self, grad_clip):
for group in self.optimizer.param_groups:
for param in group['params']:
if param.grad is not None:
param.grad.data.clamp_(-grad_clip, grad_clip)
|
class SubSystemTypes:
aperture = 'Aperture'
client = 'Client'
config = 'Config'
rights = 'Rights'
secret_store_config = 'SecretStoreconfig'
websdk = 'WebSDK'
|
user_0 = {'username': 'efermi',
'first': 'enrico',
'last': 'fermi',
}
for key, value in user_0.items():
print("\nKey: " + key)
print("Value: " + value)
print(user_0.items())
|
# Solution
def part1(data):
frequency = sum(int(x) for x in data)
return frequency
def part2(data):
known_frequency = { 0: True }
frequency = 0
while True:
for x in data:
frequency += int(x)
if frequency in known_frequency:
return frequency
known_frequency[frequency] = True
# Tests
def test(expected, actual):
assert expected == actual, 'Expected: %r, Actual: %r' % (expected, actual)
test(3, part1(['+1', '-2', '+3', '+1']))
test(3, part1(['+1', '+1', '+1']))
test(0, part1(['+1', '+1', '-2']))
test(-6, part1(['-1', '-2', '-3']))
test(2, part2(['+1', '-2', '+3', '+1']))
test(0, part2(['+1', '-1']))
test(10, part2(['+3', '+3', '+4', '-2', '-4']))
test(5, part2(['-6', '+3', '+8', '+5', '-6']))
test(14, part2(['+7', '+7', '-2', '-7', '-4']))
# Solve real puzzle
filename = 'data/day01.txt'
data = [line.rstrip('\n') for line in open(filename, 'r')]
print('Day 01, part 1: %r' % (part1(data)))
print('Day 01, part 2: %r' % (part2(data)))
|
data_file = open('us_cities.txt', 'r')
for line in data_file:
city, population = line.split(':') # Tuple unpacking
city = city.title() # Capitalize city names
population = '{0:,}'.format(int(population)) # Add commas to numbers
print(city.ljust(15) + population)
data_file.close()
|
# -*- coding: utf-8 -*-
"""
File Name： two_sum
Author : jing
Date： 2020/3/18
https://leetcode-cn.com/explore/interview/card/tencent/221/array-and-strings/894/
返回的是索引
只有一个结果
不能利用相同的数字
"""
class Solution:
def twoSum(self, nums, target: int):
if nums is None or len(nums) < 2:
return []
"""
for i in range(len(nums)):
for j in range(i+1, len(nums)):
if nums[i] + nums[j] == target:
return [i, j]
return []
"""
"""
for i in range(len(nums)):
j = nums.index(target-nums[1])
if j:
return [i, j]
"""
# dict hashtable更快！！！
dict = {}
for i in range(len(nums)):
a = target - nums[i]
if a in dict:
return dict[a], i
else:
dict[nums[i]] = i
if __name__ == '__main__':
print(Solution().twoSum([-1,-2,-3,-4,-5], -8))
|
class Veiculo:
def __init__(self, tipo) -> None:
self.tipo = tipo
self.propriedades = {}
def get_propriedades(self):
return self.propriedades
def set_propriedades(self, cor: str, cambio: str, capacidade: int) -> None:
self.propriedades = {
'cor': cor,
'cambio': cambio,
'capacidade': capacidade
}
def __str__(self) -> str:
prop_str = ", ".join([str(valor) for valor in self.get_propriedades()
.values()])
return f'{self.tipo}: {prop_str}'
class Carro(Veiculo):
def __init__(self, tipo) -> None:
super().__init__(tipo)
caminhao = Veiculo('caminhao')
caminhao.set_propriedades('azul', 'manual', 6)
carro = Carro('Sedan')
carro.set_propriedades('azul', 'automatico', 5)
kombi = Veiculo('kombi')
kombi.set_propriedades('azul', 'manual', 12)
veiculos = [caminhao, carro, kombi]
def buscar_por_cor(cor: str) -> None:
for veiculo in veiculos:
if veiculo.get_propriedades()['cor'] == cor:
print(veiculo)
buscar_por_cor('azul')
|
# -*- coding: utf-8 -*-
blupFiles = blupf90(AlphaSimDir, way='burnin_milk', sel='gen')
blupFiles.makeDat_sex(2)
shutil.copy(blupFiles.blupgenParamFile, blupFiles.AlphaSimDir) # skopiraj template blupparam file
# uredi blupparam file
# get variance components from AlphaSim Output Files
OutputFiles = AlphaSim_OutputFile(AlphaSimDir)
genvar = OutputFiles.getAddVar() # dobi additivno varianco
resvar = OutputFiles.getResVar() # dobi varianco za ostanek
blupFiles.prepareParamFiles(genvar, resvar) # set levels of random aniaml effect, add var and res var
# the paramfile is now set
if sel == 'class':
blupFiles.makePed_class() # make ped file for blup, code (1, 2, 3 - both parentr knows/unknown/group)
os.system('./blupf90 blupf90_Selection')
if sel == 'gen':
blupFiles.makePed_gen() # make ped file for blup, no Code!
GenFiles = snpFiles(AlphaSimDir)
GenFiles.createBlupf90SNPFile()
os.system('./renumf90 < renumParam') # run blupf90
# os.system('./blupf90 blupf90_Selection')
resource.setrlimit(resource.RLIMIT_STACK, (resource.RLIM_INFINITY, resource.RLIM_INFINITY))
os.system('./preGSf90 renf90.par')
os.system('./blupf90 renf90.par')
# os.system('./postGSf90 renf90.par')
# os.system('./ renf90.par')
blupSol = pd.read_csv(AlphaSimDir + '/solutions', skiprows=1, header=None, sep='\s+', names=['Trait', 'Effect', 'Level', 'Solution'])
blupSol = pd.read_csv(AlphaSimDir + '/renumbered_Solutions', header=None, sep='\s+', names=['RenumID', 'OrigID', 'Solution'])
blupSol = pd.read_csv(AlphaSimDir + '/renumbered_Solutions', skiprows=1, header=None,
sep='\s+', names=['renID', 'ID', 'Solution'])
AlphaSelPed = AlphaPed.loc[:, ['Generation', 'Indiv', 'Father', 'Mother', 'gvNormUnres1']]
#blupSolRandom = blupSol.loc[blupSol.Effect == 1] Če imaš še fixed effect
AlphaSelPed.loc[:, 'EBV'] = blupSol.Solution
AlphaSelPed.to_csv(AlphaSimDir + 'GenPed_EBV.txt', index=None)
if os.path.isfile(AlphaSimDir + 'GenoFile.txt'):
os.system('''sed -n "$(sed 's/$/p/' IndForGeno_new.txt)" ''' + chip + ' > ChosenInd.txt') #only individuals chosen for genotypisation - ONLY NEW - LAST GEN!
os.system("sed 's/^ *//' ChosenInd.txt > ChipFile.txt") #Remove blank spaces at the beginning
os.system("cut -f1 -d ' ' ChipFile.txt > Individuals.txt") # obtain IDs
os.system('''awk '{$1=""; print $0}' ChipFile.txt | sed 's/ //g' > Snps.txt''') # obtain SNP genotypes
os.system(
r'''paste Individuals.txt Snps.txt | awk '{printf "%- 10s %+ 15s\n",$1,$2}' > GenoFile_new.txt''') # obtain SNP genotypes of the last generation
os.system("cat GenoFile.txt GenoFile_new.txt > GenoFileTmp && mv GenoFileTmp GenoFile.txt")
else:
os.system('''sed -n "$(sed 's/$/p/' IndForGeno.txt)" ''' + chip + ' > ChosenInd.txt') #only individuals chosen for genotypisation - ALL
os.system("sed 's/^ *//' ChosenInd.txt > ChipFile.txt") #Remove blank spaces at the beginning
os.system("cut -f1 -d ' ' ChipFile.txt > Individuals.txt") #obtain IDs
os.system('''awk '{$1=""; print $0}' ChipFile.txt | sed 's/ //g' > Snps.txt''') #obtain SNP genotypes
os.system(
r'''paste Individuals.txt Snps.txt | awk '{printf "%- 10s %+ 15s\n",$1,$2}' > GenoFile.txt''') # obtain SNP genotypes of the last generation
pd.read_csv(AlphaSimDir + '/SimulatedData/Chip1SnpInformation.txt', sep='\s+')[[0, 1, 2]].to_csv(AlphaSimDir + 'SnpMap.txt', index=None, sep=" ", header=None)
|
turno = input("Qual perido voce estuda? V para vespertino, D para diurno ou N para noturno: ").upper()
if turno == "V":
print("Boa Tarde")
elif turno == "D":
print("Bom dia")
elif turno == "N":
print("Boav Noite")
else:
print("Entrada invalida") |
#
# This file is part of snmpresponder software.
#
# Copyright (c) 2019, Ilya Etingof <[email protected]>
# License: http://snmplabs.com/snmpresponder/license.html
#
def expandMacro(option, context):
for k in context:
pat = '${%s}' % k
if option and '${' in option:
option = option.replace(pat, str(context[k]))
return option
def expandMacros(options, context):
options = list(options)
for idx, option in enumerate(options):
options[idx] = expandMacro(option, context)
return options
|
"""Uma linha de documentação"""
variavel = 'valor'
def funcao():
return 1
|
__path__ = __import__('pkgutil').extend_path(__path__, __name__)
"""
+===================================================+
| © 2019 Privex Inc. |
| https://www.privex.io |
+===================================================+
| |
| Python Async Steem library |
| License: X11/MIT |
| |
| Core Developer(s): |
| |
| (+) Chris (@someguy123) [Privex] |
| |
+===================================================+
Async Steem library - A simple Python library for asynchronous interactions with Steem RPC nodes (and forks)
Copyright (c) 2019 Privex Inc. ( https://www.privex.io )
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy,
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of
the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS
OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Except as contained in this notice, the name(s) of the above copyright holders shall not be used in advertising or
otherwise to promote the sale, use or other dealings in this Software without prior written authorization.
"""
|
#Cristian Chitiva
#[email protected]
#16/Sept/2018
class Cat:
def __init__(self, name):
self.name = name |
class RestWriter(object):
def __init__(self, file, report):
self.file = file
self.report = report
def write(self, restsection):
assert len(restsection) >= 3
for separator, collection1 in self.report:
self.write_header(separator, restsection[0], 80)
for distribution, collection2 in collection1:
self.write_header(distribution, restsection[1], 50)
for parameters, table in collection2:
self.write_header(parameters, restsection[2], 40)
self.file.write('\n')
self.file.write(str(table))
def write_header(self, title, char, width = 80):
f = self.file
f.write('\n')
f.write('\n')
f.write("%s\n" % title)
f.write(char * max(len(title), width))
f.write('\n')
|
#!/usr/bin/python
# vim:fileencoding=utf-8:noet
# (C) 2017 Michał Górny, distributed under the terms of 2-clause BSD license
PV = '0.2.1'
|
d = DiGraph(loops=True, multiedges=True, sparse=True)
d.add_edges([(0, 0, 'a'), (0, 0, 'b'), (0, 1, 'c'),
(0, 1, 'd'), (0, 1, 'e'), (0, 1, 'f'),
(0, 1, 'f'), (2, 1, 'g'), (2, 2, 'h')])
GP = d.graphplot(vertex_size=100, edge_labels=True,
color_by_label=True, edge_style='dashed')
GP.set_edges(edge_style='solid')
GP.set_edges(edge_color='black')
sphinx_plot(GP) |
class Pessoa:
def __init__(self, nome):
self.nome = nome
@classmethod
def outro_contrutor(cls, nome, sobrenome):
cls.sobrenome = sobrenome
return cls(nome)
p = Pessoa('samuel')
print(p.nome)
p = Pessoa.outro_contrutor('saulo', 'nunes')
print(p.sobrenome)
|
#!/usr/bin/python3
"""
Main module for demo
"""
if __name__ == "__main__":
pass |
"""Exceptions for OpenZWave MQTT."""
class BaseOZWError(Exception):
"""Base OpenZWave MQTT exception."""
class NotFoundError(BaseOZWError):
"""Exception that is raised when an entity can't be found."""
class NotSupportedError(BaseOZWError):
"""Exception that is raised when an action isn't supported."""
class WrongTypeError(NotSupportedError):
"""Exception that is raised when an input is the wrong type."""
class InvalidValueError(NotSupportedError):
"""Exception that is raised when an input value is invalid."""
|
# Crie uma função que recebe como parâmetro 3 inteiros e retorna a soma dos 3.
def s(a, b, c):
soma = a + b + c
print(soma)
a = int(input('digite o numero: '))
b = int(input('digite o mnumero: '))
c = int(input('digite um numero: '))
s(a, b, c)
|
while True:
print('-=-' * 6)
n=float(input('Digite um valor (negativo para sair do programa): '))
if n<0:
break
print('-=-'*6)
for c in range(1,11):
print('\033[35m{:.0f} x {} = {:.0f}\033[m'.format(n,c,n*c))
print('\033[33mPrograma encerrado. Volte sempre!')
|
"""
PASSENGERS
"""
numPassengers = 31043
passenger_arriving = (
(10, 4, 10, 7, 4, 5, 6, 5, 3, 4, 0, 0, 0, 5, 15, 5, 4, 4, 3, 0, 2, 3, 4, 1, 0, 0), # 0
(2, 10, 11, 8, 5, 3, 3, 2, 5, 2, 0, 0, 0, 9, 10, 2, 5, 6, 4, 3, 5, 2, 8, 1, 0, 0), # 1
(1, 9, 6, 9, 8, 5, 5, 4, 1, 1, 2, 2, 0, 10, 4, 8, 11, 12, 5, 4, 2, 4, 4, 2, 4, 0), # 2
(7, 14, 5, 7, 6, 4, 0, 4, 3, 1, 3, 1, 0, 6, 8, 10, 5, 9, 7, 4, 3, 1, 0, 0, 2, 0), # 3
(11, 11, 13, 8, 7, 2, 5, 3, 3, 2, 4, 0, 0, 9, 3, 2, 9, 5, 5, 4, 3, 4, 2, 5, 0, 0), # 4
(10, 10, 5, 8, 9, 8, 3, 7, 6, 0, 5, 1, 0, 8, 16, 7, 3, 8, 5, 5, 1, 3, 5, 1, 0, 0), # 5
(13, 11, 8, 7, 8, 5, 8, 5, 7, 3, 1, 2, 0, 7, 9, 10, 9, 5, 8, 1, 1, 3, 1, 2, 1, 0), # 6
(14, 9, 7, 6, 6, 1, 5, 6, 7, 3, 2, 1, 0, 12, 12, 8, 2, 11, 6, 4, 1, 4, 1, 2, 1, 0), # 7
(12, 15, 7, 13, 10, 8, 4, 2, 2, 2, 2, 3, 0, 17, 10, 11, 9, 9, 1, 8, 2, 4, 6, 0, 2, 0), # 8
(6, 18, 9, 20, 8, 2, 8, 3, 2, 1, 0, 1, 0, 11, 14, 11, 9, 12, 9, 4, 3, 8, 7, 2, 1, 0), # 9
(18, 16, 13, 13, 6, 4, 7, 4, 7, 3, 1, 0, 0, 16, 7, 13, 6, 8, 8, 4, 3, 5, 6, 6, 1, 0), # 10
(13, 12, 13, 14, 9, 5, 4, 6, 7, 4, 2, 1, 0, 13, 16, 8, 8, 10, 8, 7, 5, 5, 2, 0, 2, 0), # 11
(15, 18, 13, 13, 12, 3, 8, 7, 3, 5, 3, 0, 0, 10, 13, 7, 11, 12, 10, 5, 4, 4, 2, 1, 3, 0), # 12
(12, 11, 10, 14, 7, 11, 5, 4, 6, 9, 2, 0, 0, 12, 13, 10, 8, 13, 11, 8, 3, 6, 3, 2, 1, 0), # 13
(15, 15, 21, 13, 12, 5, 6, 8, 5, 1, 3, 1, 0, 20, 11, 9, 7, 17, 7, 6, 7, 5, 5, 2, 1, 0), # 14
(19, 14, 10, 17, 10, 5, 9, 4, 10, 3, 1, 2, 0, 18, 19, 8, 10, 15, 3, 7, 5, 4, 1, 2, 1, 0), # 15
(11, 11, 13, 11, 11, 4, 1, 8, 7, 3, 2, 1, 0, 10, 16, 8, 6, 11, 10, 2, 4, 5, 10, 3, 0, 0), # 16
(10, 11, 8, 11, 11, 4, 5, 6, 4, 4, 3, 1, 0, 29, 14, 9, 5, 11, 3, 7, 5, 4, 6, 1, 1, 0), # 17
(13, 9, 13, 15, 5, 9, 8, 5, 5, 4, 0, 1, 0, 19, 18, 9, 9, 9, 12, 8, 6, 4, 3, 5, 1, 0), # 18
(22, 22, 14, 12, 16, 6, 9, 5, 9, 5, 2, 1, 0, 14, 20, 11, 18, 18, 2, 4, 5, 7, 2, 3, 2, 0), # 19
(14, 11, 13, 12, 12, 4, 2, 4, 6, 2, 2, 3, 0, 13, 16, 14, 5, 7, 10, 5, 4, 7, 2, 5, 1, 0), # 20
(13, 26, 17, 12, 19, 5, 5, 5, 6, 8, 4, 2, 0, 16, 16, 14, 13, 12, 8, 4, 9, 4, 7, 2, 5, 0), # 21
(8, 20, 15, 4, 10, 6, 6, 4, 10, 4, 0, 0, 0, 18, 11, 12, 7, 12, 8, 9, 2, 5, 6, 3, 1, 0), # 22
(17, 21, 17, 13, 15, 5, 10, 5, 8, 3, 2, 0, 0, 17, 11, 14, 4, 14, 8, 8, 2, 7, 2, 3, 2, 0), # 23
(23, 14, 9, 14, 14, 8, 7, 7, 6, 2, 2, 1, 0, 14, 22, 9, 10, 13, 8, 4, 4, 5, 9, 1, 0, 0), # 24
(20, 12, 12, 18, 9, 5, 12, 6, 8, 1, 3, 2, 0, 18, 14, 4, 5, 10, 13, 1, 5, 7, 7, 2, 1, 0), # 25
(15, 16, 11, 15, 12, 5, 1, 5, 9, 1, 0, 0, 0, 19, 16, 7, 13, 15, 10, 7, 7, 4, 1, 1, 0, 0), # 26
(23, 16, 11, 15, 15, 4, 7, 7, 5, 4, 3, 2, 0, 13, 18, 13, 17, 11, 9, 6, 3, 5, 3, 3, 1, 0), # 27
(19, 18, 17, 18, 12, 5, 3, 5, 9, 4, 2, 0, 0, 10, 11, 13, 11, 10, 9, 10, 3, 7, 6, 3, 2, 0), # 28
(16, 11, 16, 12, 12, 8, 8, 6, 5, 4, 3, 3, 0, 9, 10, 9, 16, 12, 13, 9, 3, 7, 8, 1, 3, 0), # 29
(12, 11, 14, 17, 13, 6, 6, 6, 5, 9, 5, 2, 0, 16, 19, 12, 13, 11, 7, 2, 6, 8, 3, 2, 2, 0), # 30
(14, 23, 17, 13, 20, 6, 6, 9, 6, 3, 1, 1, 0, 19, 16, 8, 8, 10, 8, 5, 4, 9, 4, 2, 1, 0), # 31
(18, 14, 13, 14, 15, 5, 4, 6, 5, 0, 2, 3, 0, 20, 17, 7, 11, 13, 7, 5, 4, 10, 1, 6, 3, 0), # 32
(20, 22, 18, 11, 10, 1, 10, 3, 4, 8, 1, 4, 0, 25, 17, 10, 7, 10, 5, 10, 3, 9, 5, 2, 1, 0), # 33
(21, 14, 11, 19, 11, 10, 4, 10, 9, 4, 1, 1, 0, 16, 10, 20, 15, 20, 14, 9, 3, 4, 8, 3, 1, 0), # 34
(16, 22, 17, 10, 13, 3, 5, 7, 6, 4, 4, 1, 0, 18, 18, 14, 10, 14, 9, 3, 3, 10, 4, 0, 0, 0), # 35
(11, 11, 10, 17, 7, 4, 11, 5, 8, 3, 4, 1, 0, 15, 13, 18, 11, 9, 2, 8, 7, 7, 3, 4, 0, 0), # 36
(10, 24, 13, 15, 13, 4, 3, 9, 5, 5, 3, 1, 0, 13, 16, 12, 7, 12, 10, 6, 11, 12, 4, 3, 2, 0), # 37
(19, 17, 9, 11, 9, 4, 8, 4, 4, 3, 3, 1, 0, 16, 20, 16, 14, 11, 12, 3, 0, 4, 8, 3, 2, 0), # 38
(12, 18, 18, 22, 11, 3, 5, 7, 7, 4, 2, 0, 0, 13, 24, 14, 10, 13, 12, 5, 5, 6, 4, 5, 2, 0), # 39
(15, 18, 13, 16, 13, 5, 6, 7, 4, 1, 3, 2, 0, 18, 18, 10, 11, 9, 6, 12, 1, 9, 6, 4, 2, 0), # 40
(18, 16, 9, 8, 11, 5, 9, 7, 6, 3, 3, 1, 0, 17, 14, 14, 9, 13, 6, 8, 4, 8, 6, 3, 2, 0), # 41
(14, 12, 10, 14, 9, 4, 8, 7, 4, 3, 2, 0, 0, 13, 13, 9, 13, 8, 16, 4, 3, 4, 2, 2, 1, 0), # 42
(24, 16, 13, 12, 20, 8, 7, 5, 5, 3, 1, 2, 0, 18, 12, 8, 8, 13, 8, 5, 6, 9, 5, 3, 2, 0), # 43
(16, 12, 12, 14, 18, 5, 9, 8, 3, 3, 1, 2, 0, 12, 17, 10, 8, 19, 11, 8, 6, 7, 3, 4, 2, 0), # 44
(11, 18, 14, 22, 13, 4, 11, 4, 8, 1, 3, 1, 0, 19, 13, 12, 14, 14, 3, 6, 5, 6, 4, 1, 3, 0), # 45
(21, 10, 14, 15, 7, 5, 6, 5, 10, 4, 0, 5, 0, 22, 14, 12, 10, 16, 9, 7, 3, 6, 6, 2, 1, 0), # 46
(14, 22, 14, 19, 15, 3, 9, 7, 5, 3, 0, 0, 0, 12, 21, 11, 8, 11, 6, 6, 9, 10, 2, 2, 2, 0), # 47
(28, 15, 8, 15, 14, 6, 5, 9, 12, 1, 0, 1, 0, 11, 13, 6, 9, 16, 3, 6, 3, 6, 0, 2, 3, 0), # 48
(10, 18, 17, 17, 14, 6, 6, 4, 5, 6, 1, 1, 0, 18, 15, 11, 10, 12, 7, 7, 4, 4, 3, 1, 0, 0), # 49
(13, 14, 8, 20, 13, 12, 6, 5, 3, 5, 5, 0, 0, 16, 14, 11, 5, 17, 7, 7, 5, 6, 5, 5, 1, 0), # 50
(19, 17, 22, 27, 16, 4, 2, 3, 7, 4, 1, 1, 0, 19, 10, 10, 9, 11, 10, 6, 2, 5, 6, 5, 1, 0), # 51
(12, 12, 19, 11, 9, 8, 7, 7, 8, 1, 1, 3, 0, 17, 18, 9, 6, 13, 8, 5, 4, 13, 3, 2, 1, 0), # 52
(10, 15, 13, 11, 13, 5, 5, 7, 4, 2, 1, 2, 0, 15, 15, 6, 6, 16, 4, 5, 6, 6, 4, 1, 3, 0), # 53
(24, 20, 16, 13, 5, 10, 8, 11, 2, 1, 2, 0, 0, 19, 18, 13, 7, 17, 11, 7, 5, 4, 3, 0, 0, 0), # 54
(18, 18, 14, 17, 11, 6, 6, 3, 5, 4, 0, 0, 0, 11, 19, 10, 5, 13, 10, 5, 4, 6, 5, 1, 2, 0), # 55
(20, 13, 14, 20, 15, 7, 7, 2, 7, 7, 3, 0, 0, 16, 19, 11, 6, 15, 11, 4, 4, 7, 8, 4, 1, 0), # 56
(16, 13, 20, 10, 6, 8, 3, 6, 7, 1, 1, 1, 0, 19, 17, 10, 9, 14, 4, 6, 5, 9, 6, 2, 2, 0), # 57
(19, 15, 10, 20, 8, 7, 6, 9, 3, 7, 3, 0, 0, 18, 15, 6, 8, 13, 5, 7, 4, 5, 3, 7, 4, 0), # 58
(14, 10, 13, 20, 15, 5, 4, 6, 2, 1, 2, 0, 0, 20, 13, 14, 9, 16, 7, 6, 1, 9, 2, 2, 2, 0), # 59
(13, 14, 7, 15, 9, 6, 5, 5, 3, 2, 2, 3, 0, 17, 16, 11, 2, 14, 5, 10, 4, 6, 6, 1, 1, 0), # 60
(16, 16, 13, 13, 11, 9, 6, 2, 8, 3, 2, 0, 0, 18, 9, 7, 12, 18, 6, 6, 3, 6, 5, 4, 2, 0), # 61
(15, 15, 15, 6, 10, 8, 5, 4, 5, 2, 2, 3, 0, 21, 14, 7, 8, 15, 14, 5, 3, 4, 8, 3, 2, 0), # 62
(16, 8, 23, 17, 10, 5, 4, 12, 9, 2, 2, 0, 0, 13, 21, 15, 9, 19, 4, 8, 7, 3, 5, 3, 1, 0), # 63
(18, 13, 22, 10, 6, 5, 4, 4, 3, 4, 2, 2, 0, 16, 16, 9, 5, 12, 6, 7, 6, 7, 6, 3, 1, 0), # 64
(15, 14, 15, 14, 16, 6, 9, 4, 5, 2, 6, 2, 0, 16, 9, 20, 7, 14, 8, 5, 4, 7, 8, 2, 1, 0), # 65
(19, 14, 22, 15, 14, 7, 7, 6, 8, 4, 3, 1, 0, 21, 20, 11, 8, 13, 4, 8, 5, 5, 5, 4, 0, 0), # 66
(16, 17, 13, 15, 9, 6, 3, 8, 6, 1, 6, 0, 0, 16, 21, 16, 11, 13, 6, 10, 5, 8, 3, 1, 2, 0), # 67
(20, 13, 13, 17, 12, 4, 7, 9, 6, 4, 4, 6, 0, 17, 14, 7, 7, 10, 10, 6, 2, 7, 2, 4, 1, 0), # 68
(15, 21, 13, 18, 11, 10, 5, 3, 5, 4, 2, 2, 0, 16, 12, 7, 15, 15, 4, 10, 3, 5, 4, 4, 1, 0), # 69
(15, 13, 17, 8, 9, 7, 5, 2, 6, 1, 5, 0, 0, 20, 9, 14, 3, 17, 6, 10, 8, 3, 6, 1, 0, 0), # 70
(12, 16, 13, 17, 15, 11, 11, 4, 11, 6, 2, 0, 0, 15, 14, 7, 11, 10, 0, 7, 4, 6, 2, 3, 1, 0), # 71
(20, 14, 12, 15, 13, 4, 8, 5, 3, 4, 1, 3, 0, 14, 16, 15, 13, 7, 6, 12, 8, 10, 10, 3, 0, 0), # 72
(17, 16, 16, 15, 16, 8, 6, 4, 4, 3, 4, 1, 0, 21, 16, 12, 14, 19, 7, 3, 7, 6, 5, 0, 1, 0), # 73
(17, 12, 15, 12, 12, 6, 6, 6, 6, 2, 2, 0, 0, 12, 17, 10, 9, 15, 5, 2, 7, 8, 8, 3, 2, 0), # 74
(13, 10, 14, 16, 12, 10, 7, 2, 8, 2, 3, 1, 0, 20, 8, 11, 11, 14, 4, 7, 3, 5, 1, 4, 0, 0), # 75
(19, 22, 10, 17, 7, 6, 10, 5, 6, 2, 4, 1, 0, 11, 16, 11, 4, 13, 7, 8, 3, 10, 1, 5, 3, 0), # 76
(9, 16, 18, 8, 7, 8, 4, 5, 2, 2, 5, 2, 0, 19, 16, 14, 10, 17, 7, 9, 2, 4, 5, 2, 0, 0), # 77
(16, 21, 13, 7, 13, 9, 8, 4, 4, 2, 5, 0, 0, 15, 20, 13, 5, 11, 16, 7, 3, 5, 6, 3, 1, 0), # 78
(18, 10, 14, 11, 10, 8, 11, 3, 12, 1, 2, 2, 0, 16, 12, 10, 8, 9, 8, 7, 4, 7, 4, 2, 1, 0), # 79
(14, 19, 9, 16, 19, 7, 6, 8, 8, 1, 5, 1, 0, 13, 14, 5, 5, 11, 6, 3, 5, 6, 6, 2, 3, 0), # 80
(11, 13, 10, 10, 18, 4, 4, 2, 3, 1, 1, 1, 0, 13, 2, 13, 7, 11, 4, 6, 3, 3, 4, 4, 3, 0), # 81
(16, 17, 11, 12, 11, 6, 6, 4, 7, 2, 5, 0, 0, 15, 14, 14, 19, 14, 2, 5, 1, 7, 3, 4, 1, 0), # 82
(15, 13, 15, 23, 10, 6, 3, 4, 3, 0, 1, 3, 0, 14, 18, 11, 8, 11, 11, 6, 1, 10, 3, 2, 0, 0), # 83
(17, 11, 14, 17, 16, 4, 5, 4, 5, 3, 1, 0, 0, 17, 15, 13, 6, 9, 5, 2, 1, 4, 6, 1, 0, 0), # 84
(18, 14, 16, 11, 10, 4, 4, 4, 6, 2, 2, 1, 0, 14, 11, 9, 4, 9, 8, 7, 3, 9, 2, 5, 0, 0), # 85
(13, 19, 11, 15, 9, 6, 8, 4, 5, 4, 1, 4, 0, 11, 19, 8, 5, 13, 4, 8, 7, 9, 2, 3, 2, 0), # 86
(10, 11, 13, 17, 12, 5, 9, 4, 10, 4, 3, 0, 0, 17, 15, 10, 9, 9, 10, 7, 3, 8, 6, 1, 1, 0), # 87
(18, 12, 15, 16, 7, 5, 1, 2, 8, 2, 3, 4, 0, 18, 18, 9, 3, 18, 8, 8, 1, 8, 3, 5, 0, 0), # 88
(21, 15, 17, 14, 14, 5, 5, 8, 6, 2, 0, 2, 0, 15, 12, 8, 12, 15, 3, 5, 2, 5, 4, 4, 2, 0), # 89
(18, 13, 15, 9, 10, 8, 4, 7, 5, 4, 3, 0, 0, 14, 8, 11, 5, 11, 9, 4, 2, 4, 8, 5, 2, 0), # 90
(16, 12, 12, 20, 10, 6, 6, 4, 1, 2, 2, 2, 0, 17, 11, 5, 9, 12, 8, 5, 3, 4, 5, 1, 0, 0), # 91
(22, 16, 9, 14, 7, 4, 5, 4, 10, 3, 2, 1, 0, 12, 9, 10, 12, 11, 10, 3, 4, 3, 5, 2, 1, 0), # 92
(19, 13, 20, 11, 12, 6, 8, 8, 12, 1, 1, 0, 0, 18, 17, 6, 3, 11, 5, 7, 2, 9, 6, 2, 2, 0), # 93
(19, 15, 11, 21, 8, 7, 4, 1, 6, 2, 2, 0, 0, 21, 14, 10, 6, 21, 6, 7, 3, 6, 3, 4, 0, 0), # 94
(15, 15, 12, 24, 14, 6, 8, 3, 10, 3, 3, 2, 0, 24, 12, 11, 7, 10, 7, 7, 5, 6, 4, 2, 0, 0), # 95
(13, 15, 14, 9, 18, 8, 4, 4, 6, 7, 2, 0, 0, 14, 11, 5, 9, 16, 11, 5, 6, 5, 4, 1, 3, 0), # 96
(12, 7, 15, 14, 18, 5, 4, 6, 7, 2, 6, 3, 0, 19, 16, 6, 6, 19, 2, 7, 4, 4, 8, 2, 2, 0), # 97
(11, 8, 8, 9, 9, 5, 3, 4, 6, 2, 2, 1, 0, 16, 19, 12, 5, 7, 11, 5, 6, 6, 3, 2, 1, 0), # 98
(10, 13, 11, 13, 14, 4, 4, 2, 10, 3, 1, 1, 0, 16, 14, 9, 6, 13, 3, 2, 3, 6, 5, 0, 0, 0), # 99
(20, 11, 10, 14, 12, 5, 7, 3, 7, 3, 4, 1, 0, 12, 10, 12, 1, 15, 4, 8, 6, 5, 7, 1, 2, 0), # 100
(11, 10, 11, 10, 15, 5, 5, 3, 7, 1, 1, 0, 0, 16, 12, 8, 10, 15, 9, 4, 5, 2, 4, 6, 2, 0), # 101
(12, 12, 13, 11, 11, 9, 6, 6, 6, 2, 3, 1, 0, 17, 12, 9, 9, 15, 6, 5, 5, 12, 6, 1, 0, 0), # 102
(16, 18, 12, 18, 8, 7, 2, 4, 9, 1, 1, 5, 0, 15, 17, 13, 4, 16, 2, 6, 4, 4, 3, 3, 3, 0), # 103
(17, 11, 12, 11, 13, 8, 6, 3, 5, 5, 3, 1, 0, 19, 5, 9, 6, 12, 9, 5, 5, 3, 10, 1, 0, 0), # 104
(13, 15, 12, 12, 11, 9, 4, 3, 4, 1, 4, 1, 0, 16, 17, 12, 2, 16, 6, 6, 3, 6, 7, 2, 0, 0), # 105
(11, 13, 12, 14, 7, 9, 7, 8, 7, 1, 2, 1, 0, 16, 11, 8, 5, 10, 7, 4, 4, 7, 1, 2, 1, 0), # 106
(11, 8, 11, 17, 16, 5, 6, 3, 7, 1, 2, 2, 0, 11, 13, 6, 4, 13, 6, 3, 2, 6, 10, 1, 1, 0), # 107
(17, 10, 9, 15, 15, 4, 5, 4, 12, 4, 2, 1, 0, 12, 14, 9, 7, 16, 3, 6, 4, 4, 1, 6, 1, 0), # 108
(26, 11, 14, 21, 13, 5, 5, 5, 3, 1, 5, 1, 0, 17, 10, 7, 6, 8, 8, 8, 3, 5, 2, 2, 1, 0), # 109
(14, 14, 21, 20, 21, 5, 1, 4, 2, 2, 1, 3, 0, 12, 14, 12, 9, 15, 3, 5, 3, 5, 5, 3, 0, 0), # 110
(19, 16, 19, 14, 14, 8, 2, 1, 7, 2, 2, 3, 0, 20, 10, 7, 9, 11, 6, 4, 4, 3, 5, 1, 1, 0), # 111
(20, 12, 10, 9, 15, 2, 6, 4, 8, 5, 2, 1, 0, 20, 8, 7, 8, 11, 8, 5, 3, 6, 8, 2, 2, 0), # 112
(11, 13, 17, 14, 16, 9, 6, 4, 7, 3, 2, 0, 0, 14, 16, 10, 6, 11, 7, 4, 2, 6, 2, 2, 2, 0), # 113
(13, 13, 13, 10, 13, 3, 3, 1, 4, 3, 5, 1, 0, 10, 15, 6, 9, 11, 1, 7, 3, 6, 3, 2, 0, 0), # 114
(10, 14, 17, 12, 14, 8, 5, 2, 5, 4, 1, 1, 0, 17, 10, 17, 9, 9, 10, 3, 5, 8, 3, 2, 0, 0), # 115
(14, 7, 14, 14, 17, 8, 5, 6, 3, 3, 1, 0, 0, 10, 11, 10, 8, 15, 6, 5, 7, 9, 6, 3, 1, 0), # 116
(11, 15, 15, 12, 13, 5, 3, 5, 7, 2, 1, 1, 0, 18, 8, 15, 11, 7, 8, 1, 4, 6, 5, 2, 1, 0), # 117
(17, 6, 15, 11, 12, 5, 5, 2, 5, 5, 2, 1, 0, 24, 7, 10, 6, 16, 2, 6, 9, 3, 4, 1, 1, 0), # 118
(17, 16, 17, 13, 7, 5, 4, 7, 4, 0, 3, 1, 0, 15, 10, 5, 7, 9, 1, 3, 5, 4, 3, 3, 1, 0), # 119
(11, 9, 14, 14, 15, 3, 3, 2, 6, 2, 1, 0, 0, 17, 11, 8, 9, 12, 9, 1, 1, 4, 6, 1, 2, 0), # 120
(12, 12, 14, 7, 14, 5, 3, 2, 7, 2, 2, 1, 0, 12, 11, 12, 7, 6, 4, 2, 0, 5, 5, 1, 0, 0), # 121
(11, 9, 14, 14, 15, 9, 9, 3, 6, 3, 0, 1, 0, 13, 7, 4, 6, 14, 6, 10, 4, 5, 6, 0, 0, 0), # 122
(8, 14, 19, 15, 17, 2, 1, 4, 5, 2, 2, 1, 0, 18, 11, 13, 11, 6, 10, 3, 4, 8, 2, 2, 0, 0), # 123
(14, 8, 12, 16, 8, 2, 5, 2, 6, 4, 1, 0, 0, 22, 11, 7, 8, 12, 9, 5, 4, 6, 0, 1, 0, 0), # 124
(17, 13, 10, 18, 10, 4, 9, 3, 5, 3, 0, 4, 0, 23, 12, 10, 7, 20, 9, 3, 7, 7, 6, 1, 0, 0), # 125
(12, 9, 9, 7, 10, 2, 6, 3, 6, 1, 2, 1, 0, 9, 16, 4, 10, 13, 2, 1, 5, 6, 2, 3, 0, 0), # 126
(14, 11, 6, 11, 15, 3, 5, 3, 8, 1, 2, 2, 0, 12, 9, 8, 7, 5, 7, 9, 5, 7, 4, 4, 1, 0), # 127
(13, 11, 13, 12, 14, 6, 5, 5, 3, 2, 1, 1, 0, 20, 12, 12, 6, 11, 13, 3, 6, 8, 1, 5, 2, 0), # 128
(18, 7, 12, 12, 13, 2, 3, 5, 7, 0, 1, 2, 0, 15, 11, 13, 12, 15, 6, 6, 1, 6, 4, 3, 1, 0), # 129
(8, 8, 12, 17, 11, 3, 7, 2, 2, 3, 0, 0, 0, 10, 7, 9, 10, 14, 6, 1, 3, 5, 4, 2, 1, 0), # 130
(13, 10, 17, 10, 9, 5, 4, 1, 4, 3, 2, 2, 0, 14, 10, 8, 11, 15, 7, 6, 3, 5, 2, 0, 0, 0), # 131
(22, 6, 13, 10, 11, 4, 9, 5, 10, 2, 3, 0, 0, 11, 15, 4, 7, 12, 13, 4, 2, 3, 5, 2, 2, 0), # 132
(8, 18, 15, 11, 13, 6, 4, 6, 11, 3, 0, 3, 0, 13, 13, 10, 11, 7, 7, 3, 5, 4, 2, 1, 0, 0), # 133
(14, 17, 10, 8, 11, 2, 8, 5, 7, 2, 3, 3, 0, 13, 8, 11, 8, 11, 4, 2, 3, 3, 3, 0, 1, 0), # 134
(7, 10, 11, 14, 7, 15, 7, 3, 3, 2, 0, 0, 0, 15, 6, 10, 4, 9, 3, 3, 6, 6, 6, 1, 1, 0), # 135
(13, 11, 9, 6, 10, 2, 0, 4, 4, 4, 2, 0, 0, 15, 9, 10, 7, 11, 3, 5, 1, 10, 1, 2, 0, 0), # 136
(15, 7, 11, 9, 10, 2, 2, 3, 4, 4, 1, 0, 0, 14, 14, 5, 3, 11, 6, 6, 4, 4, 5, 2, 2, 0), # 137
(6, 11, 14, 15, 9, 7, 4, 2, 0, 0, 3, 0, 0, 7, 12, 8, 8, 12, 3, 5, 4, 4, 1, 1, 1, 0), # 138
(10, 8, 13, 7, 11, 3, 4, 7, 5, 2, 3, 0, 0, 18, 13, 4, 8, 8, 3, 3, 3, 3, 6, 5, 2, 0), # 139
(14, 16, 13, 12, 13, 3, 0, 3, 6, 1, 2, 1, 0, 10, 7, 5, 7, 8, 5, 1, 3, 8, 5, 1, 1, 0), # 140
(14, 12, 18, 10, 12, 4, 2, 2, 7, 0, 1, 0, 0, 11, 4, 12, 7, 15, 6, 5, 1, 4, 5, 4, 2, 0), # 141
(11, 9, 14, 12, 6, 6, 2, 4, 8, 2, 4, 0, 0, 17, 12, 12, 5, 9, 8, 5, 2, 6, 9, 4, 0, 0), # 142
(15, 9, 10, 16, 7, 6, 7, 2, 2, 3, 2, 1, 0, 20, 11, 13, 9, 12, 7, 1, 10, 10, 3, 2, 2, 0), # 143
(17, 11, 7, 16, 14, 6, 4, 2, 6, 0, 1, 2, 0, 14, 12, 9, 7, 17, 5, 4, 2, 4, 5, 3, 0, 0), # 144
(12, 11, 12, 13, 8, 5, 5, 5, 6, 2, 1, 0, 0, 14, 11, 6, 9, 11, 7, 4, 4, 6, 5, 3, 1, 0), # 145
(9, 7, 9, 7, 6, 5, 4, 3, 6, 2, 1, 0, 0, 20, 8, 11, 12, 10, 5, 4, 5, 6, 4, 1, 1, 0), # 146
(14, 9, 10, 13, 8, 1, 4, 2, 7, 4, 1, 0, 0, 17, 15, 6, 6, 12, 5, 3, 4, 5, 3, 3, 0, 0), # 147
(12, 4, 8, 16, 10, 4, 5, 4, 5, 1, 1, 1, 0, 11, 7, 7, 6, 13, 3, 2, 3, 3, 3, 2, 2, 0), # 148
(13, 11, 17, 12, 8, 3, 5, 3, 4, 3, 4, 2, 0, 16, 8, 11, 4, 13, 4, 4, 3, 5, 3, 2, 1, 0), # 149
(10, 7, 19, 10, 8, 4, 7, 4, 6, 1, 3, 0, 0, 13, 10, 10, 8, 12, 5, 3, 4, 9, 3, 2, 0, 0), # 150
(16, 10, 7, 11, 11, 3, 5, 9, 3, 2, 2, 0, 0, 16, 5, 9, 6, 9, 2, 1, 1, 5, 6, 6, 0, 0), # 151
(11, 12, 6, 12, 9, 2, 3, 5, 5, 2, 1, 2, 0, 7, 9, 3, 4, 12, 6, 2, 4, 7, 5, 5, 0, 0), # 152
(11, 6, 10, 9, 8, 3, 3, 3, 7, 4, 1, 0, 0, 11, 14, 2, 11, 12, 4, 4, 8, 5, 4, 1, 0, 0), # 153
(14, 11, 7, 12, 12, 5, 7, 8, 7, 3, 2, 1, 0, 9, 15, 14, 4, 2, 7, 4, 6, 1, 5, 6, 2, 0), # 154
(11, 9, 10, 9, 14, 8, 5, 3, 3, 1, 1, 1, 0, 15, 11, 3, 6, 10, 4, 6, 3, 5, 6, 2, 0, 0), # 155
(14, 7, 14, 15, 8, 9, 5, 2, 5, 1, 4, 2, 0, 7, 10, 8, 3, 10, 5, 5, 4, 1, 5, 0, 1, 0), # 156
(10, 11, 13, 14, 6, 13, 3, 1, 7, 3, 0, 2, 0, 15, 10, 13, 5, 18, 5, 4, 2, 7, 4, 8, 1, 0), # 157
(14, 9, 7, 12, 7, 7, 3, 4, 7, 3, 3, 1, 0, 16, 11, 5, 4, 11, 8, 2, 3, 5, 9, 2, 0, 0), # 158
(11, 10, 9, 13, 14, 4, 3, 2, 2, 5, 0, 0, 0, 19, 6, 5, 5, 10, 5, 3, 2, 6, 7, 4, 2, 0), # 159
(8, 6, 11, 10, 13, 7, 3, 5, 6, 4, 0, 1, 0, 13, 10, 2, 3, 8, 8, 5, 4, 6, 2, 6, 0, 0), # 160
(6, 8, 15, 12, 9, 8, 3, 3, 8, 2, 1, 1, 0, 15, 7, 11, 3, 14, 6, 3, 4, 6, 1, 3, 1, 0), # 161
(12, 9, 14, 5, 10, 2, 3, 6, 2, 2, 1, 1, 0, 5, 13, 11, 2, 9, 4, 4, 1, 5, 2, 0, 0, 0), # 162
(8, 7, 8, 18, 5, 4, 3, 5, 1, 1, 0, 2, 0, 14, 6, 9, 3, 8, 5, 3, 3, 3, 2, 3, 1, 0), # 163
(13, 11, 7, 7, 10, 3, 5, 5, 5, 1, 1, 0, 0, 3, 8, 7, 5, 11, 3, 1, 3, 6, 6, 1, 1, 0), # 164
(16, 16, 6, 15, 7, 3, 2, 3, 3, 1, 0, 3, 0, 14, 19, 7, 7, 10, 3, 1, 5, 3, 1, 1, 2, 0), # 165
(13, 6, 8, 13, 16, 2, 4, 9, 4, 0, 1, 0, 0, 8, 8, 4, 4, 7, 5, 2, 5, 5, 5, 1, 0, 0), # 166
(10, 10, 9, 6, 7, 2, 1, 2, 5, 2, 3, 1, 0, 10, 12, 12, 6, 3, 4, 4, 5, 5, 1, 1, 0, 0), # 167
(11, 6, 6, 10, 12, 2, 1, 4, 6, 2, 2, 2, 0, 14, 13, 7, 7, 10, 2, 3, 5, 7, 4, 0, 0, 0), # 168
(17, 3, 5, 9, 7, 4, 2, 2, 5, 2, 0, 0, 0, 16, 9, 7, 5, 11, 4, 0, 4, 5, 7, 0, 0, 0), # 169
(10, 8, 8, 11, 6, 4, 2, 3, 5, 3, 1, 1, 0, 8, 10, 3, 4, 12, 3, 5, 2, 1, 3, 2, 0, 0), # 170
(6, 4, 4, 4, 5, 4, 3, 2, 2, 0, 0, 1, 0, 7, 6, 6, 2, 9, 4, 2, 4, 5, 3, 1, 0, 0), # 171
(8, 6, 14, 3, 8, 6, 2, 2, 5, 0, 1, 1, 0, 12, 5, 4, 3, 10, 1, 2, 2, 0, 5, 2, 1, 0), # 172
(12, 7, 11, 8, 6, 6, 4, 1, 4, 1, 0, 0, 0, 9, 10, 5, 2, 11, 3, 3, 2, 5, 2, 0, 0, 0), # 173
(6, 6, 9, 5, 5, 3, 3, 0, 3, 2, 0, 0, 0, 9, 9, 2, 2, 11, 2, 6, 4, 7, 2, 2, 1, 0), # 174
(4, 6, 6, 4, 9, 3, 3, 1, 6, 1, 1, 0, 0, 7, 7, 7, 4, 9, 6, 0, 2, 4, 2, 2, 1, 0), # 175
(7, 4, 10, 6, 4, 3, 2, 1, 4, 0, 0, 0, 0, 7, 15, 5, 2, 6, 2, 4, 1, 4, 3, 4, 0, 0), # 176
(7, 1, 2, 4, 4, 0, 2, 2, 2, 0, 0, 0, 0, 9, 6, 2, 6, 3, 3, 2, 3, 2, 0, 2, 0, 0), # 177
(3, 2, 5, 3, 3, 0, 5, 3, 4, 0, 2, 0, 0, 3, 4, 5, 6, 4, 3, 5, 1, 2, 1, 5, 1, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(8.033384925394829, 8.840461695509067, 8.33805316738001, 9.943468438181492, 8.887496972175379, 5.021847891259743, 6.6336569845982645, 7.445081876767077, 9.744158499468812, 6.332824024835792, 6.728424262216965, 7.836664125289878, 8.134208340125381), # 0
(8.566923443231959, 9.424097110631614, 8.888554546128244, 10.600230805242587, 9.475984539958779, 5.353573734468089, 7.07115030602191, 7.9352219566491335, 10.387592522132655, 6.75036910764344, 7.172953817529811, 8.353946657302968, 8.671666635903767), # 1
(9.09875681436757, 10.005416273425567, 9.436867656875862, 11.254380327463672, 10.062340757999591, 5.683976183219912, 7.506909612737127, 8.423400396647072, 11.028458891004078, 7.166262040032874, 7.615717038042101, 8.869172243284888, 9.206983725135505), # 2
(9.6268124690345, 10.582112803098315, 9.980817390911767, 11.903322252051318, 10.644258681603043, 6.011744996136181, 7.939205826636729, 8.907681851991212, 11.664216257473749, 7.578852317481889, 8.054957458923813, 9.380297095888738, 9.738036490006762), # 3
(10.149017837465571, 11.15188031885724, 10.518228639524859, 12.544461826212112, 11.219431366074389, 6.335569931837869, 8.366309869613534, 9.386130977911865, 12.292323272932332, 7.986489435468286, 8.48891861534492, 9.885277427767623, 10.262701812703709), # 4
(10.663300349893618, 11.712412439909741, 11.04692629400403, 13.17520429715263, 11.785551866718848, 6.654140748945943, 8.786492663560358, 9.856812429639348, 12.910238588770495, 8.387522889469862, 8.915844042475412, 10.382069451574637, 10.778856575412524), # 5
(11.167587436551466, 12.261402785463202, 11.564735245638186, 13.792954912079445, 12.34031323884167, 6.9661472060813825, 9.19802513037002, 10.317790862403982, 13.515420856378904, 8.780302174964413, 9.333977275485251, 10.868629379962893, 11.284377660319372), # 6
(11.65980652767195, 12.79654497472501, 12.069480385716217, 14.39511891819914, 12.881408537748086, 7.270279061865153, 9.599178191935335, 10.767130931436084, 14.105328727148231, 9.16317678742974, 9.74156184954443, 11.342913425585486, 11.777141949610431), # 7
(12.137885053487896, 13.31553262690256, 12.558986605527034, 14.979101562718284, 13.406530818743338, 7.565226074918224, 9.988222770149116, 11.20289729196596, 14.67742085246913, 9.53449622234364, 10.136841299822914, 11.802877801095525, 12.255026325471867), # 8
(12.599750444232136, 13.816059361203237, 13.031078796359527, 15.54230809284347, 13.913373137132655, 7.849678003861574, 10.363429786904192, 11.623154599223941, 15.229155883732279, 9.892609975183907, 10.518059161490685, 12.246478719146102, 12.71590767008986), # 9
(13.043330130137491, 14.295818796834425, 13.483581849502599, 16.08214375578126, 14.399628548221282, 8.122324607316171, 10.723070164093368, 12.025967508440338, 15.757992472328343, 10.235867541428343, 10.883458969717719, 12.671672392390324, 13.157662865650577), # 10
(13.466551541436809, 14.752504553003531, 13.914320656245145, 16.596013798738237, 14.862990107314454, 8.38185564390299, 11.065414823609466, 12.409400674845465, 16.26138926964799, 10.56261841655475, 11.231284259673998, 13.076415033481297, 13.57816879434018), # 11
(13.8673421083629, 15.183810248917917, 14.321120107876064, 17.08132346892098, 15.301150869717404, 8.626960872242991, 11.388734687345298, 12.771518753669634, 16.736804927081888, 10.871212096040916, 11.559778566529495, 13.45866285507211, 13.975302338344855), # 12
(14.243629261148602, 15.587429503784993, 14.701805095684259, 17.53547801353607, 15.711803890735363, 8.856330050957158, 11.69130067719369, 13.11038640014317, 17.181698096020693, 11.159998075364648, 11.86718542545419, 13.816372069815873, 14.346940379850777), # 13
(14.593340430026746, 15.961055936812143, 15.054200510958635, 17.95588267979007, 16.092642225673583, 9.068652938666455, 11.971383715047459, 13.424068269496395, 17.593527427855076, 11.427325850003735, 12.151748371618055, 14.147498890365696, 14.690959801044102), # 14
(14.914403045230168, 16.30238316720675, 15.376131244988068, 18.339942714889578, 16.441358929837293, 9.26261929399186, 12.227254722799401, 13.71062901695961, 17.96975157397571, 11.671544915435986, 12.411710940191071, 14.449999529374674, 15.00523748411101), # 15
(15.204744536991681, 16.609104814176213, 15.66542218906148, 18.685063366041145, 16.755647058531732, 9.436918875554335, 12.457184622342362, 13.968133297763139, 18.307829185773258, 11.891004767139194, 12.64531666634322, 14.721830199495905, 15.287650311237673), # 16
(15.46229233554412, 16.878914496927916, 15.919898234467764, 18.98864988045138, 17.033199667062142, 9.590241441974857, 12.659444335569138, 14.19464576713731, 18.605218914638375, 12.084054900591148, 12.850809085244478, 14.960947113382488, 15.536075164610265), # 17
(15.684973871120327, 17.10950583466924, 16.137384272495808, 19.248107505326846, 17.271709810733743, 9.721276751874406, 12.832304784372562, 14.388231080312417, 18.859379411961754, 12.249044811269659, 13.026431732064815, 15.165306483687544, 15.748388926414954), # 18
(15.870716573953118, 17.29857244660759, 16.315705194434525, 19.460841487874106, 17.468870544851786, 9.828714563873934, 12.974036890645431, 14.546953892518793, 19.067769329134048, 12.384323994652526, 13.170428141974206, 15.332864523064154, 15.922468478837914), # 19
(16.01744787427533, 17.44380795195034, 16.452685891572806, 19.624257075299766, 17.62237492472151, 9.91124463659443, 13.08291157628058, 14.668878858986748, 19.22784731754592, 12.488241946217535, 13.28104185014264, 15.461577444165426, 16.05619070406532), # 20
(16.123095202319785, 17.542905969904893, 16.54615125519955, 19.73575951481038, 17.729916005648143, 9.967556728656858, 13.157199763170816, 14.752070634946598, 19.337072028588036, 12.559148161442488, 13.356516391740096, 15.54940145964447, 16.147432484283325), # 21
(16.18558598831933, 17.59356011967863, 16.593926176603656, 19.79275405361254, 17.78918684293692, 9.996340598682188, 13.19517237320896, 14.794593875628664, 19.392902113651065, 12.595392135805188, 13.395095301936545, 15.594292782154383, 16.194070701678125), # 22
(16.208629381348224, 17.599557750342935, 16.599877091906723, 19.799889300411525, 17.804371289652156, 10.0, 13.199686403614942, 14.79919012345679, 19.399881975308645, 12.599667636031093, 13.399932859458785, 15.599836122542294, 16.2), # 23
(16.225619860854646, 17.59605925925926, 16.598903703703705, 19.799011111111113, 17.812972181783763, 10.0, 13.197206100217867, 14.7928, 19.398946666666667, 12.59704098765432, 13.39939932659933, 15.598538271604937, 16.2), # 24
(16.242251568338528, 17.589163237311386, 16.59698216735254, 19.797273662551444, 17.821383912951205, 10.0, 13.192318244170096, 14.78024691358025, 19.3970987654321, 12.591870141746686, 13.39834143908218, 15.595976223136716, 16.2), # 25
(16.258523230476854, 17.578975034293556, 16.594138820301787, 19.79469670781893, 17.82960618947377, 10.0, 13.185098749293955, 14.76176790123457, 19.39436197530864, 12.58424113397348, 13.396768774161368, 15.592185093735715, 16.2), # 26
(16.27443357394662, 17.5656, 16.5904, 19.7913, 17.837638717670742, 10.0, 13.175623529411766, 14.7376, 19.39076, 12.57424, 13.39469090909091, 15.587200000000003, 16.2), # 27
(16.2899813254248, 17.549143484224967, 16.585792043895747, 19.787103292181072, 17.845481203861443, 10.0, 13.163968498345842, 14.707980246913582, 19.386316543209876, 12.561952775491541, 13.39211742112483, 15.581056058527665, 16.2), # 28
(16.3051652115884, 17.52971083676269, 16.580341289437587, 19.78212633744856, 17.853133354365152, 10.0, 13.150209569918506, 14.673145679012345, 19.381055308641976, 12.547465496113398, 13.389057887517147, 15.57378838591678, 16.2), # 29
(16.319983959114396, 17.50740740740741, 16.574074074074073, 19.77638888888889, 17.860594875501178, 10.0, 13.13442265795207, 14.633333333333333, 19.375, 12.530864197530866, 13.385521885521886, 15.56543209876543, 16.2), # 30
(16.334436294679772, 17.482338545953365, 16.567016735253773, 19.76991069958848, 17.867865473588814, 10.0, 13.116683676268863, 14.588780246913581, 19.368174320987656, 12.512234915409238, 13.381518992393067, 15.556022313671699, 16.2), # 31
(16.34852094496153, 17.45460960219479, 16.55919561042524, 19.762711522633747, 17.874944854947355, 10.0, 13.097068538691198, 14.539723456790126, 19.360601975308644, 12.49166368541381, 13.377058785384712, 15.545594147233656, 16.2), # 32
(16.362236636636634, 17.424325925925924, 16.55063703703704, 19.75481111111111, 17.8818327258961, 10.0, 13.075653159041394, 14.486400000000001, 19.352306666666667, 12.469236543209878, 13.372150841750841, 15.534182716049381, 16.2), # 33
(16.375582096382097, 17.391592866941014, 16.541367352537723, 19.746229218106997, 17.888528792754347, 10.0, 13.052513451141776, 14.429046913580246, 19.343312098765438, 12.445039524462736, 13.36680473874548, 15.521823136716964, 16.2), # 34
(16.388556050874893, 17.356515775034293, 16.53141289437586, 19.736985596707818, 17.895032761841392, 10.0, 13.027725328814654, 14.367901234567903, 19.333641975308645, 12.419158664837678, 13.361030053622645, 15.508550525834478, 16.2), # 35
(16.40115722679201, 17.3192, 16.5208, 19.7271, 17.901344339476537, 10.0, 13.001364705882352, 14.303200000000002, 19.32332, 12.391680000000001, 13.354836363636364, 15.494400000000002, 16.2), # 36
(16.41338435081044, 17.27975089163237, 16.50955500685871, 19.71659218106996, 17.907463231979076, 10.0, 12.97350749616719, 14.23518024691358, 19.31236987654321, 12.362689565615, 13.348233246040657, 15.479406675811616, 16.2), # 37
(16.425236149607162, 17.238273799725654, 16.49770425240055, 19.70548189300412, 17.913389145668305, 10.0, 12.944229613491487, 14.164079012345681, 19.300815308641976, 12.332273397347967, 13.341230278089538, 15.4636056698674, 16.2), # 38
(16.436711349859177, 17.194874074074075, 16.485274074074077, 19.69378888888889, 17.919121786863524, 10.0, 12.913606971677561, 14.090133333333334, 19.288680000000003, 12.300517530864198, 13.333837037037037, 15.447032098765431, 16.2), # 39
(16.44780867824346, 17.149657064471878, 16.472290809327845, 19.6815329218107, 17.924660861884032, 10.0, 12.88171548454773, 14.013580246913584, 19.27598765432099, 12.267508001828991, 13.326063100137175, 15.429721079103798, 16.2), # 40
(16.458526861437004, 17.102728120713305, 16.458780795610426, 19.66873374485597, 17.930006077049125, 10.0, 12.848631065924312, 13.934656790123459, 19.262761975308642, 12.233330845907636, 13.317918044643973, 15.411707727480568, 16.2), # 41
(16.4688646261168, 17.054192592592596, 16.444770370370374, 19.655411111111114, 17.935157138678093, 10.0, 12.814429629629629, 13.8536, 19.24902666666667, 12.198072098765433, 13.30941144781145, 15.393027160493828, 16.2), # 42
(16.47882069895983, 17.00415582990398, 16.430285871056242, 19.641584773662554, 17.940113753090245, 10.0, 12.779187089486001, 13.770646913580249, 19.234805432098767, 12.161817796067673, 13.300552886893627, 15.373714494741657, 16.2), # 43
(16.488393806643085, 16.9527231824417, 16.4153536351166, 19.62727448559671, 17.944875626604873, 10.0, 12.742979359315743, 13.686034567901238, 19.220121975308643, 12.124653973479653, 13.291351939144532, 15.353804846822133, 16.2), # 44
(16.497582675843546, 16.900000000000002, 16.400000000000002, 19.6125, 17.949442465541274, 10.0, 12.705882352941178, 13.600000000000001, 19.205, 12.086666666666668, 13.281818181818181, 15.333333333333332, 16.2), # 45
(16.50638603323821, 16.846091632373113, 16.384251303155008, 19.59728106995885, 17.953813976218747, 10.0, 12.667971984184621, 13.512780246913582, 19.189463209876543, 12.04794191129401, 13.271961192168598, 15.312335070873344, 16.2), # 46
(16.514802605504055, 16.79110342935528, 16.36813388203018, 19.581637448559672, 17.957989864956588, 10.0, 12.629324166868395, 13.424612345679012, 19.173535308641977, 12.008565743026978, 13.261790547449806, 15.29084517604024, 16.2), # 47
(16.522831119318074, 16.735140740740743, 16.351674074074076, 19.565588888888893, 17.961969838074097, 10.0, 12.590014814814815, 13.335733333333335, 19.15724, 11.968624197530865, 13.251315824915824, 15.268898765432098, 16.2), # 48
(16.53047030135726, 16.67830891632373, 16.334898216735255, 19.549155144032923, 17.965753601890572, 10.0, 12.550119841846204, 13.246380246913581, 19.14060098765432, 11.928203310470966, 13.240546601820677, 15.246530955647007, 16.2), # 49
(16.537718878298588, 16.620713305898494, 16.31783264746228, 19.53235596707819, 17.969340862725304, 10.0, 12.50971516178488, 13.15679012345679, 19.12364197530864, 11.887389117512575, 13.22949245541838, 15.223776863283039, 16.2), # 50
(16.544575576819057, 16.56245925925926, 16.300503703703704, 19.515211111111114, 17.9727313268976, 10.0, 12.46887668845316, 13.0672, 19.10638666666667, 11.846267654320988, 13.218162962962964, 15.200671604938274, 16.2), # 51
(16.551039123595647, 16.503652126200276, 16.282937722908095, 19.497740329218107, 17.975924700726743, 10.0, 12.427680335673365, 12.977846913580246, 19.0888587654321, 11.8049249565615, 13.206567701708444, 15.177250297210794, 16.2), # 52
(16.55710824530535, 16.444397256515778, 16.26516104252401, 19.479963374485596, 17.978920690532046, 10.0, 12.386202017267813, 12.888967901234569, 19.071081975308644, 11.763447059899406, 13.194716248908842, 15.153548056698675, 16.2), # 53
(16.562781668625146, 16.384800000000002, 16.2472, 19.4619, 17.981719002632804, 10.0, 12.344517647058824, 12.800799999999999, 19.05308, 11.72192, 13.18261818181818, 15.1296, 16.2), # 54
(16.568058120232035, 16.324965706447188, 16.229080932784637, 19.443569958847736, 17.984319343348304, 10.0, 12.302703138868717, 12.71358024691358, 19.034876543209876, 11.68042981252858, 13.170283077690485, 15.10544124371285, 16.2), # 55
(16.572936326802996, 16.264999725651577, 16.210830178326475, 19.424993004115226, 17.986721418997856, 10.0, 12.26083440651981, 12.627545679012346, 19.016495308641975, 11.639062533150437, 13.157720513779774, 15.0811069044353, 16.2), # 56
(16.577415015015013, 16.205007407407408, 16.192474074074077, 19.40618888888889, 17.988924935900748, 10.0, 12.218987363834422, 12.542933333333336, 18.997960000000003, 11.597904197530866, 13.144940067340068, 15.056632098765432, 16.2), # 57
(16.581492911545087, 16.145094101508917, 16.174038957475997, 19.387177366255145, 17.99092960037628, 10.0, 12.177237924634875, 12.459980246913581, 18.979294320987655, 11.557040841335164, 13.131951315625393, 15.032051943301326, 16.2), # 58
(16.585168743070195, 16.085365157750342, 16.155551165980796, 19.367978189300413, 17.992735118743752, 10.0, 12.135662002743485, 12.378923456790124, 18.960521975308644, 11.516558500228626, 13.11876383588976, 15.007401554641062, 16.2), # 59
(16.588441236267325, 16.02592592592593, 16.137037037037036, 19.34861111111111, 17.99434119732246, 10.0, 12.094335511982571, 12.3, 18.94166666666667, 11.476543209876544, 13.105387205387206, 14.982716049382717, 16.2), # 60
(16.591309117813463, 15.966881755829906, 16.11852290809328, 19.329095884773665, 17.995747542431697, 10.0, 12.053334366174454, 12.223446913580247, 18.922752098765432, 11.437081005944217, 13.091831001371743, 14.958030544124373, 16.2), # 61
(16.593771114385607, 15.908337997256517, 16.100035116598082, 19.30945226337449, 17.996953860390775, 10.0, 12.01273447914145, 12.149501234567902, 18.903801975308642, 11.398257924096939, 13.078104801097394, 14.933380155464107, 16.2), # 62
(16.595825952660736, 15.8504, 16.0816, 19.289700000000003, 17.99795985751897, 10.0, 11.972611764705881, 12.078400000000002, 18.88484, 11.36016, 13.064218181818184, 14.9088, 16.2), # 63
(16.597472359315837, 15.793173113854596, 16.0632438957476, 19.26985884773663, 17.998765240135597, 10.0, 11.933042136690068, 12.010380246913583, 18.86588987654321, 11.322873269318702, 13.050180720788127, 14.884325194330135, 16.2), # 64
(16.5987090610279, 15.73676268861454, 16.04499314128944, 19.249948559670784, 17.999369714559947, 10.0, 11.894101508916325, 11.945679012345678, 18.846975308641976, 11.286483767718336, 13.036001995261257, 14.859990855052581, 16.2), # 65
(16.599534784473914, 15.681274074074077, 16.026874074074076, 19.22998888888889, 17.999772987111317, 10.0, 11.855865795206972, 11.884533333333335, 18.828120000000002, 11.251077530864197, 13.021691582491583, 14.835832098765435, 16.2), # 66
(16.59994825633087, 15.626812620027435, 16.00891303155007, 19.209999588477366, 17.99997476410901, 10.0, 11.81841090938433, 11.827180246913583, 18.809347654320987, 11.216740594421584, 13.007259059733137, 14.811884042066758, 16.2), # 67
(16.59966658316932, 15.573197822912517, 15.991049519890261, 19.189826784755773, 17.999804728475752, 9.99981441853376, 11.781624311727434, 11.77335016003658, 18.790540557841794, 11.183392706635466, 12.992457581664603, 14.788048035039589, 16.19980024005487), # 68
(16.597026731078905, 15.51879283154122, 15.97278148148148, 19.168453623188405, 17.99825708061002, 9.998347325102882, 11.744429090154583, 11.720158024691358, 18.770876543209877, 11.150090225127087, 12.975780542264753, 14.76355035737492, 16.198217592592595), # 69
(16.59181726009423, 15.463347935749368, 15.954029492455417, 19.14573939881911, 17.995198902606308, 9.995458009449779, 11.706656215298192, 11.667123914037496, 18.750244627343395, 11.116671239140375, 12.957038218441728, 14.738276418068494, 16.195091735253776), # 70
(16.584111457028687, 15.406896269746449, 15.93480013717421, 19.12171760601181, 17.990668926006617, 9.991193293705228, 11.668322655262381, 11.61426538637403, 18.728675537265662, 11.083136574948224, 12.936299793254179, 14.712244699540344, 16.190463820301783), # 71
(16.573982608695655, 15.349470967741935, 15.915099999999999, 19.096421739130435, 17.98470588235294, 9.985600000000002, 11.62944537815126, 11.5616, 18.706200000000003, 11.04948705882353, 12.913634449760767, 14.685473684210528, 16.184375), # 72
(16.561504001908514, 15.291105163945307, 15.894935665294923, 19.069885292538917, 17.977348503187283, 9.978724950464867, 11.590041352068948, 11.50914531321445, 18.682848742569732, 11.01572351703919, 12.889111371020142, 14.65798185449907, 16.1768664266118), # 73
(16.546748923480646, 15.231831992566043, 15.874313717421124, 19.04214176060118, 17.96863552005164, 9.970614967230606, 11.550127545119556, 11.456918884316416, 18.658652491998172, 10.9818467758681, 12.86279974009097, 14.629787692826028, 16.167979252400553), # 74
(16.52979066022544, 15.171684587813619, 15.85324074074074, 19.01322463768116, 17.95860566448802, 9.961316872427986, 11.509720925407201, 11.404938271604939, 18.63364197530864, 10.947857661583152, 12.834768740031897, 14.600909681611435, 16.157754629629633), # 75
(16.510702498956285, 15.11069608389752, 15.831723319615913, 18.98316741814278, 17.94729766803841, 9.950877488187778, 11.468838461035993, 11.353221033379059, 18.607847919524463, 10.913757000457247, 12.805087553901586, 14.571366303275333, 16.146233710562413), # 76
(16.48955772648655, 15.048899615027217, 15.809768038408777, 18.95200359634997, 17.934750262244815, 9.939343636640757, 11.427497120110047, 11.301784727937816, 18.581301051668955, 10.87954561876328, 12.7738253647587, 14.54117604023777, 16.13345764746228), # 77
(16.46642962962963, 14.98632831541219, 15.787381481481482, 18.919766666666668, 17.92100217864924, 9.926762139917695, 11.38571387073348, 11.250646913580248, 18.55403209876543, 10.845224342774147, 12.741051355661883, 14.510357374918781, 16.119467592592596), # 78
(16.441391495198904, 14.923015319261916, 15.76457023319616, 18.88649012345679, 17.906092148793675, 9.913179820149367, 11.343505681010402, 11.199825148605397, 18.52607178783722, 10.810793998762742, 12.706834709669796, 14.478928789738408, 16.104304698216733), # 79
(16.414516610007755, 14.858993760785877, 15.74134087791495, 18.852207461084273, 17.890058904220126, 9.898643499466544, 11.30088951904493, 11.149336991312301, 18.497450845907636, 10.776255413001962, 12.671244609841102, 14.446908767116696, 16.08801011659808), # 80
(16.385878260869568, 14.79429677419355, 15.7177, 18.816952173913048, 17.872941176470587, 9.8832, 11.257882352941177, 11.099200000000002, 18.4682, 10.741609411764706, 12.63435023923445, 14.414315789473685, 16.070625), # 81
(16.355549734597723, 14.728957493694413, 15.693654183813445, 18.780757756307032, 17.854777697087066, 9.866896143880508, 11.214501150803258, 11.049431732967536, 18.43834997713763, 10.706856821323866, 12.596220780908501, 14.381168339229419, 16.052190500685874), # 82
(16.323604318005607, 14.663009053497943, 15.669210013717422, 18.743657702630166, 17.835607197611555, 9.849778753238837, 11.170762880735285, 11.000049748513947, 18.40793150434385, 10.671998467952339, 12.55692541792191, 14.34748489880394, 16.03274777091907), # 83
(16.290115297906603, 14.59648458781362, 15.644374074074074, 18.70568550724638, 17.815468409586057, 9.831894650205761, 11.126684510841374, 10.95107160493827, 18.376975308641974, 10.637035177923023, 12.516533333333333, 14.313283950617285, 16.012337962962963), # 84
(16.255155961114095, 14.529417230850923, 15.61915294924554, 18.666874664519593, 17.794400064552573, 9.813290656912057, 11.08228300922564, 10.902514860539554, 18.345512117055325, 10.60196777750881, 12.47511371020143, 14.2785839770895, 15.991002229080934), # 85
(16.21879959444146, 14.46184011681933, 15.593553223593966, 18.627258668813745, 17.772440894053094, 9.794013595488494, 11.037575343992193, 10.854397073616827, 18.313572656607228, 10.566797092982599, 12.432735731584856, 14.24340346064063, 15.968781721536352), # 86
(16.18111948470209, 14.393786379928315, 15.567581481481481, 18.586871014492754, 17.749629629629634, 9.774110288065843, 10.99257848324515, 10.806735802469136, 18.28118765432099, 10.531523950617284, 12.389468580542264, 14.207760883690709, 15.945717592592594), # 87
(16.142188918709373, 14.325289154387361, 15.541244307270233, 18.54574519592056, 17.726005002824177, 9.753627556774882, 10.947309395088626, 10.75954860539552, 18.248387837219937, 10.496149176685762, 12.345381440132318, 14.171674728659784, 15.921850994513035), # 88
(16.102081183276677, 14.256381574405948, 15.51454828532236, 18.503914707461085, 17.701605745178732, 9.732612223746381, 10.901785047626733, 10.712853040695016, 18.21520393232739, 10.460673597460932, 12.30054349341367, 14.135163477967897, 15.897223079561043), # 89
(16.06086956521739, 14.187096774193549, 15.4875, 18.461413043478263, 17.676470588235297, 9.711111111111112, 10.856022408963586, 10.666666666666666, 18.18166666666667, 10.425098039215687, 12.255023923444977, 14.098245614035088, 15.871875000000001), # 90
(16.0186273513449, 14.117467887959643, 15.460106035665294, 18.41827369833602, 17.650638263535864, 9.689171040999847, 10.810038447203299, 10.621007041609511, 18.14780676726109, 10.389423328222922, 12.208891913284896, 14.060939619281399, 15.845847908093276), # 91
(15.975427828472597, 14.047528049913716, 15.432372976680384, 18.374530166398284, 17.624147502622446, 9.666838835543363, 10.763850130449988, 10.57589172382259, 18.113654961133975, 10.353650290755535, 12.162216645992086, 14.023263976126877, 15.819182956104251), # 92
(15.931344283413848, 13.977310394265235, 15.404307407407408, 18.33021594202899, 17.597037037037037, 9.644161316872427, 10.717474426807762, 10.53133827160494, 18.079241975308644, 10.31777975308642, 12.1150673046252, 13.985237166991553, 15.791921296296294), # 93
(15.886450002982048, 13.906848055223684, 15.375915912208507, 18.285364519592058, 17.569345598321632, 9.621185307117818, 10.670928304380737, 10.487364243255604, 18.044598536808415, 10.281812541488476, 12.067513072242896, 13.946877674295479, 15.764104080932785), # 94
(15.840818273990577, 13.836174166998541, 15.347205075445817, 18.240009393451423, 17.541111918018238, 9.597957628410304, 10.62422873127303, 10.443987197073618, 18.00975537265661, 10.245749482234594, 12.019623131903835, 13.908203980458689, 15.735772462277092), # 95
(15.79452238325282, 13.765321863799286, 15.318181481481483, 18.194184057971015, 17.512374727668846, 9.574525102880658, 10.577392675588754, 10.401224691358026, 17.974743209876543, 10.209591401597677, 11.971466666666668, 13.869234567901238, 15.706967592592594), # 96
(15.747635617582157, 13.694324279835394, 15.28885171467764, 18.14792200751476, 17.483172758815464, 9.550934552659655, 10.530437105432021, 10.359094284407867, 17.939592775491544, 10.173339125850616, 11.923112859590052, 13.829987919043152, 15.677730624142663), # 97
(15.700231263791975, 13.623214549316343, 15.259222359396432, 18.101256736446594, 17.453544743000084, 9.52723279987807, 10.48337898890695, 10.317613534522177, 17.904334796524918, 10.136993481266307, 11.87463089373265, 13.790482516304477, 15.648102709190674), # 98
(15.652382608695653, 13.552025806451613, 15.229300000000002, 18.054221739130437, 17.423529411764708, 9.503466666666666, 10.43623529411765, 10.276800000000001, 17.869, 10.100555294117648, 11.826089952153112, 13.750736842105264, 15.618125000000001), # 99
(15.60416293910658, 13.480791185450682, 15.19909122085048, 18.00685050993022, 17.393165496651335, 9.479682975156226, 10.389022989168232, 10.236671239140376, 17.833619112940102, 10.064025390677534, 11.777559217910095, 13.710769378865548, 15.58783864883402), # 100
(15.555645541838135, 13.409543820523034, 15.168602606310015, 17.959176543209878, 17.36249172920197, 9.455928547477518, 10.34175904216282, 10.19724481024234, 17.798222862368544, 10.027404597218862, 11.72910787406226, 13.670598609005365, 15.557284807956103), # 101
(15.506903703703706, 13.338316845878138, 15.13784074074074, 17.911233333333335, 17.331546840958605, 9.432250205761319, 10.294460421205521, 10.15853827160494, 17.762841975308643, 9.990693740014526, 11.680805103668263, 13.63024301494477, 15.526504629629631), # 102
(15.458010711516671, 13.267143395725476, 15.1068122085048, 17.86305437466452, 17.300369563463246, 9.408694772138395, 10.247144094400449, 10.120569181527207, 17.72750717878372, 9.953893645337423, 11.632720089786758, 13.589721079103796, 15.495539266117968), # 103
(15.409039852090416, 13.196056604274526, 15.075523593964334, 17.814673161567367, 17.268998628257886, 9.385309068739522, 10.199827029851722, 10.083355098308186, 17.692249199817102, 9.91700513946045, 11.584922015476401, 13.549051283902486, 15.464429869684501), # 104
(15.360064412238325, 13.125089605734766, 15.043981481481481, 17.766123188405796, 17.237472766884533, 9.362139917695474, 10.152526195663453, 10.046913580246915, 17.6570987654321, 9.880029048656501, 11.537480063795854, 13.508252111760886, 15.433217592592593), # 105
(15.311157678773782, 13.054275534315678, 15.012192455418381, 17.717437949543747, 17.205830710885177, 9.339234141137021, 10.105258559939752, 10.011262185642433, 17.622086602652033, 9.842966199198472, 11.490463417803769, 13.46734204509903, 15.401943587105624), # 106
(15.26239293851017, 12.983647524226738, 14.980163100137176, 17.66865093934514, 17.174111191801824, 9.31663856119494, 10.058041090784739, 9.976418472793783, 17.58724343850023, 9.805817417359263, 11.443941260558804, 13.426339566336967, 15.370649005486968), # 107
(15.21384347826087, 12.913238709677422, 14.947900000000002, 17.619795652173917, 17.14235294117647, 9.294400000000001, 10.010890756302521, 9.942400000000001, 17.5526, 9.768583529411766, 11.397982775119617, 13.38526315789474, 15.339375000000002), # 108
(15.16558258483927, 12.843082224877207, 14.915409739369, 17.570905582393987, 17.11059469055112, 9.272565279682976, 9.96382452459722, 9.90922432556013, 17.518187014174668, 9.731265361628877, 11.352657144544864, 13.34413130219238, 15.308162722908094), # 109
(15.117683545058746, 12.77321120403558, 14.882698902606315, 17.522014224369297, 17.078875171467768, 9.251181222374639, 9.916859363772943, 9.876909007773206, 17.484035208047555, 9.693863740283494, 11.308033551893201, 13.302962481649942, 15.277053326474624), # 110
(15.07021964573269, 12.703658781362009, 14.849774074074077, 17.47315507246377, 17.047233115468412, 9.230294650205762, 9.87001224193381, 9.845471604938272, 17.450175308641978, 9.656379491648512, 11.264181180223286, 13.261775178687461, 15.246087962962964), # 111
(15.02326417367448, 12.634458091065975, 14.816641838134434, 17.42436162104133, 17.015707254095055, 9.209952385307119, 9.823300127183934, 9.814929675354367, 17.41663804298125, 9.618813441996826, 11.221169212593775, 13.220587875724977, 15.215307784636488), # 112
(14.976806757924871, 12.565757790057525, 14.78338852520331, 17.375734211987265, 16.98428108827793, 9.190191630743222, 9.776841541850832, 9.78536411004897, 17.383540498013794, 9.581287578580367, 11.179078249844586, 13.179508698407085, 15.184710241349155), # 113
(14.930369436640104, 12.498235493640857, 14.75047308003459, 17.327663074043738, 16.952629367306123, 9.170967373647843, 9.731229133456928, 9.757138015208191, 17.351390457140898, 9.544504268660452, 11.137990939381115, 13.13905947538076, 15.154040662656056), # 114
(14.883815844806392, 12.431915517892875, 14.717915092331708, 17.280135208290847, 16.920652284621763, 9.152229619998023, 9.6864954403065, 9.730244246845935, 17.320199965870064, 9.508520524780923, 11.09784721828335, 13.099260132094162, 15.123210610656603), # 115
(14.837087797180216, 12.366701250066724, 14.685651503974197, 17.233065840426246, 16.888301642214046, 9.133934203659356, 9.64256770804463, 9.70460850063839, 17.28989014276453, 9.473269373519276, 11.05856949003437, 13.060037115979753, 15.092171615609425), # 116
(14.790127108518035, 12.302496077415555, 14.653619256841578, 17.18637019614759, 16.855529242072176, 9.116036958497425, 9.599373182316404, 9.680156472261736, 17.260382106387524, 9.438683841453006, 11.020080158117253, 13.021316874470001, 15.06087520777316), # 117
(14.742875593576338, 12.239203387192518, 14.621755292813388, 17.139963501152533, 16.82228688618535, 9.098493718377823, 9.556839108766905, 9.656813857392155, 17.231596975302296, 9.404696955159615, 10.98230162601508, 12.98302585499736, 15.02927291740644), # 118
(14.695275067111588, 12.176726566650768, 14.589996553769158, 17.09376098113873, 16.788526376542755, 9.081260317166132, 9.51489273304121, 9.634506351705832, 17.20345586807207, 9.371241741216595, 10.945156297210925, 12.945090504994296, 14.997316274767892), # 119
(14.647267343880259, 12.114969003043454, 14.55827998158842, 17.04767786180383, 16.754199515133596, 9.064292588727945, 9.473461300784406, 9.613159650878949, 17.175879903260093, 9.338251226201448, 10.908566575187866, 12.907437271893276, 14.964956810116156), # 120
(14.59879423863883, 12.053834083623727, 14.5265425181507, 17.001629368845496, 16.71925810394707, 9.047546366928849, 9.432472057641569, 9.592699450587691, 17.148790199429598, 9.305658436691674, 10.872454863428986, 12.869992603126756, 14.932146053709857), # 121
(14.549797566143766, 11.993225195644738, 14.494721105335538, 16.95553072796137, 16.683653944972374, 9.03097748563443, 9.391852249257788, 9.573051446508238, 17.122107875143822, 9.273396399264763, 10.836743565417363, 12.832682946127202, 14.898835535807633), # 122
(14.50021914115155, 11.933045726359639, 14.462752685022458, 16.90929716484911, 16.647338840198707, 9.01454177871028, 9.351529121278142, 9.554141334316773, 17.095754048966008, 9.24139814049822, 10.801355084636072, 12.795434748327075, 14.864976786668116), # 123
(14.450000778418648, 11.87319906302158, 14.430574199090993, 16.86284390520638, 16.61026459161526, 8.998195080021983, 9.311429919347711, 9.535894809689482, 17.069649839459384, 9.209596686969538, 10.766211824568192, 12.758174457158841, 14.830521336549939), # 124
(14.399084292701534, 11.813588592883713, 14.398122589420678, 16.816086174730817, 16.572383001211236, 8.98189322343513, 9.271481889111582, 9.518237568302546, 17.04371636518719, 9.177925065256215, 10.731236188696803, 12.720828520054958, 14.795420715711726), # 125
(14.347411498756685, 11.754117703199192, 14.365334797891038, 16.768939199120087, 16.53364587097583, 8.965592042815308, 9.231612276214832, 9.501095305832148, 17.017874744712667, 9.146316301935748, 10.696350580504982, 12.683323384447895, 14.759626454412127), # 126
(14.294924211340579, 11.69468978122116, 14.332147766381608, 16.72131820407184, 16.494005002898238, 8.949247372028104, 9.19174832630255, 9.484393717954474, 16.99204609659905, 9.114703423585638, 10.661477403475807, 12.645585497770107, 14.723090082909758), # 127
(14.241564245209673, 11.635208214202777, 14.29849843677192, 16.67313841528373, 16.453412198967666, 8.93281504493911, 9.151817285019812, 9.4680585003457, 16.966151539409577, 9.083019456783381, 10.626539061092359, 12.607541307454062, 14.68576313146326), # 128
(14.187273415120451, 11.575576389397186, 14.264323750941504, 16.624315058453412, 16.4118192611733, 8.916250895413912, 9.111746398011702, 9.452015348682016, 16.94011219170748, 9.051197428106473, 10.591457956837715, 12.569117260932218, 14.647597130331262), # 129
(14.131993535829388, 11.515697694057547, 14.229560650769887, 16.57476335927854, 16.36917799150434, 8.899510757318094, 9.0714629109233, 9.4361899586396, 16.913849172056, 9.019170364132412, 10.556156494194951, 12.530239805637045, 14.608543609772397), # 130
(14.07566642209295, 11.455475515437003, 14.19414607813661, 16.524398543456762, 16.32544019194999, 8.88255046451725, 9.030894069399695, 9.42050802589464, 16.887283599018378, 8.986871291438696, 10.52055707664715, 12.490835389000999, 14.568554100045299), # 131
(14.018233888667616, 11.39481324078871, 14.158016974921194, 16.47313583668574, 16.280557664499447, 8.865325850876964, 8.98996711908596, 9.404895246123317, 16.860336591157846, 8.954233236602823, 10.484582107677383, 12.450830458456547, 14.527580131408602), # 132
(13.959637750309861, 11.333614257365817, 14.121110283003175, 16.420890464663124, 16.2344822111419, 8.847792750262826, 8.948609305627183, 9.389277315001811, 16.832929267037642, 8.921189226202292, 10.448153990768738, 12.410151461436149, 14.485573234120938), # 133
(13.899819821776152, 11.271781952421478, 14.083362944262086, 16.367577653086567, 16.18716563386655, 8.829906996540425, 8.906747874668445, 9.37357992820631, 16.804982745221007, 8.887672286814597, 10.411195129404286, 12.368724845372267, 14.442484938440934), # 134
(13.838721917822966, 11.209219713208839, 14.044711900577454, 16.313112627653727, 16.138559734662593, 8.811624423575347, 8.86431007185483, 9.357728781412993, 16.77641814427117, 8.853615445017242, 10.373627927067108, 12.326477057697364, 14.398266774627231), # 135
(13.776285853206776, 11.145830926981056, 14.005094093828815, 16.25741061406225, 16.08861631551923, 8.792900865233184, 8.821223142831416, 9.341649570298044, 16.74715658275137, 8.818951727387716, 10.335374787240283, 12.283334545843907, 14.352870272938459), # 136
(13.712453442684055, 11.081518980991277, 13.964446465895698, 16.200386838009802, 16.037287178425654, 8.773692155379518, 8.77741433324329, 9.325267990537647, 16.717119179224852, 8.783614160503523, 10.296358113406889, 12.239223757244352, 14.306246963633242), # 137
(13.647166501011277, 11.016187262492654, 13.922705958657628, 16.141956525194022, 15.98452412537107, 8.753954127879942, 8.732810888735527, 9.308509737807984, 16.68622705225485, 8.747535770942156, 10.256500309050004, 12.194071139331164, 14.258348376970226), # 138
(13.58036684294491, 10.949739158738339, 13.879809513994145, 16.082034901312575, 15.930278958344665, 8.733642616600042, 8.687340054953216, 9.29130050778524, 16.654401320404595, 8.710649585281116, 10.215723777652705, 12.14780313953681, 14.20912604320803), # 139
(13.511996283241437, 10.88207805698148, 13.83569407378478, 16.020537192063113, 15.874503479335647, 8.712713455405407, 8.640929077541434, 9.273565996145594, 16.62156310223733, 8.672888630097898, 10.17395092269807, 12.100346205293746, 14.158531492605304), # 140
(13.44199663665733, 10.813107344475235, 13.790296579909057, 15.957378623143285, 15.817149490333206, 8.691122478161624, 8.593505202145272, 9.255231898565233, 16.587633516316288, 8.634185931970002, 10.131104147669182, 12.05162678403444, 14.106516255420662), # 141
(13.37030971794905, 10.742730408472745, 13.743553974246513, 15.892474420250753, 15.75816879332654, 8.668825518734284, 8.544995674409803, 9.236223910720339, 16.552533681204707, 8.594474517474925, 10.087105856049115, 12.001571323191351, 14.053031861912746), # 142
(13.29687734187308, 10.67085063622717, 13.695403198676681, 15.82573980908316, 15.697513190304846, 8.64577841098897, 8.49532773998011, 9.21646772828709, 16.516184715465837, 8.553687413190165, 10.04187845132095, 11.950106270196944, 13.998029842340188), # 143
(13.221641323185896, 10.597371414991658, 13.645781195079085, 15.757090015338171, 15.635134483257326, 8.621936988791274, 8.444428644501278, 9.195889046941678, 16.478507737662895, 8.511757645693216, 9.995344336967761, 11.897158072483679, 13.941461726961624), # 144
(13.144543476643964, 10.52219613201936, 13.594624905333262, 15.686440264713433, 15.570984474173173, 8.597257086006785, 8.39222563361839, 9.174413562360282, 16.439423866359128, 8.46861824156158, 9.947425916472632, 11.842653177484022, 13.88327904603568), # 145
(13.065525617003761, 10.445228174563427, 13.541871271318747, 15.613705782906601, 15.505014965041589, 8.57169453650109, 8.338645952976528, 9.151966970219084, 16.39885422011777, 8.424202227372753, 9.898045593318638, 11.786518032630433, 13.82343332982099), # 146
(12.98452955902176, 10.366370929877009, 13.487457234915055, 15.538801795615328, 15.437177757851764, 8.545205174139772, 8.28361684822077, 9.128474966194265, 16.356719917502065, 8.378442629704233, 9.847125770988859, 11.728679085355378, 13.761876108576189), # 147
(12.901497117454435, 10.285527785213262, 13.431319738001733, 15.461643528537275, 15.367424654592899, 8.517744832788429, 8.227065564996202, 9.103863245962012, 16.312942077075245, 8.331272475133515, 9.794588852966372, 11.669062783091313, 13.698558912559907), # 148
(12.81637010705826, 10.20260212782533, 13.37339572245831, 15.382146207370084, 15.295707457254194, 8.48926934631264, 8.168919348947906, 9.078057505198506, 16.26744181740054, 8.282624790238101, 9.740357242734255, 11.607595573270707, 13.63343327203078), # 149
(12.729090342589704, 10.117497344966367, 13.313622130164312, 15.30022505781142, 15.221977967824841, 8.459734548577998, 8.109105445720962, 9.05098343957993, 16.220140257041205, 8.232432601595482, 9.684353343775589, 11.544203903326022, 13.566450717247434), # 150
(12.63959963880524, 10.030116823889527, 13.251935902999268, 15.215795305558927, 15.146187988294043, 8.429096273450089, 8.047551100960453, 9.02256674478247, 16.170958514560464, 8.180628935783165, 9.626499559573448, 11.478814220689715, 13.49756277846851), # 151
(12.54783981046135, 9.940363951847957, 13.188273982842723, 15.128772176310271, 15.06828932065099, 8.397310354794502, 7.984183560311464, 8.992733116482306, 16.119817708521552, 8.12714681937864, 9.566718293610915, 11.411352972794255, 13.426720985952636), # 152
(12.453752672314497, 9.848142116094811, 13.12257331157419, 15.039070895763093, 14.988233766884889, 8.364332626476825, 7.918930069419071, 8.96140825035562, 16.06663895748772, 8.071919278959406, 9.504931949371066, 11.341746607072103, 13.353876869958444), # 153
(12.357280039121166, 9.75335470388324, 13.054770831073213, 14.946606689615056, 14.905973128984929, 8.330118922362647, 7.851717873928365, 8.928517842078596, 16.011343380022186, 8.014879341102965, 9.44106293033698, 11.26992157095572, 13.278981960744572), # 154
(12.258363725637818, 9.655905102466392, 12.984803483219322, 14.851294783563805, 14.821459208940315, 8.294625076317555, 7.782474219484418, 8.893987587327418, 15.953852094688205, 7.955960032386807, 9.375033639991733, 11.195804311877572, 13.201987788569642), # 155
(12.15694554662093, 9.555696699097421, 12.912608209892042, 14.753050403307, 14.734643808740238, 8.257806922207138, 7.71112635173232, 8.85774318177827, 15.894086220049003, 7.8950943793884365, 9.306766481818407, 11.119321277270117, 13.122845883692296), # 156
(12.05296731682698, 9.452632881029478, 12.838121952970909, 14.6517887745423, 14.645478730373895, 8.219620293896982, 7.637601516317151, 8.819710321107332, 15.831966874667822, 7.832215408685347, 9.236183859300079, 11.04039891456582, 13.041507776371162), # 157
(11.943489514248384, 9.344724993235614, 12.75774712624377, 14.54363133064199, 14.549889769393596, 8.177639162107376, 7.560170753484572, 8.777275123758995, 15.762659346558557, 7.76538546606583, 9.160953204062308, 10.956159302710944, 12.954377375064553), # 158
(11.811658827165445, 9.220904511359164, 12.65078050944478, 14.406363454061527, 14.424306095650605, 8.117903436811366, 7.469140421417146, 8.715541652423012, 15.658283617955432, 7.683649590557993, 9.06786709699039, 10.850180037892974, 12.840684235072311), # 159
(11.655795351846896, 9.080154765665142, 12.515073532729422, 14.237724016654177, 14.266272210154874, 8.038946073676295, 7.363589997414055, 8.632958703243755, 15.515880363565842, 7.58592904298063, 8.955615213775264, 10.720803118220555, 12.69827297422973), # 160
(11.477155287337537, 8.92339338892875, 12.352075155056495, 14.039316006010765, 14.077428998851381, 7.941723586512502, 7.244290313611002, 8.530560852975649, 15.337327627198428, 7.473053109073501, 8.825186647359532, 10.569227950252113, 12.528598471710556), # 161
(11.27699483268217, 8.751538013925183, 12.163234335384793, 13.812742409722123, 13.859417347685127, 7.827192489130329, 7.112012202143695, 8.409382678373124, 15.12450345266182, 7.3458510745763705, 8.677570490685794, 10.39665394054607, 12.333115606688533), # 162
(11.056570186925597, 8.565506273429639, 11.950000032673124, 13.559606215379095, 13.613878142601102, 7.696309295340116, 6.967526495147841, 8.2704587561906, 14.87928588376465, 7.205152225229, 8.513755836696653, 10.204280495660853, 12.113279258337407), # 163
(10.817137549112616, 8.366215800217313, 11.713821205880283, 13.281510410572508, 13.342452269544303, 7.550030518952207, 6.811604024759146, 8.114823663182511, 14.603552964315558, 7.05178584677115, 8.334731778334714, 9.993307022154886, 11.870544305830926), # 164
(10.559953118288028, 8.154584227063411, 11.45614681396507, 12.980057982893204, 13.046780614459719, 7.389312673776939, 6.6450156231133155, 7.943511976103274, 14.299182738123168, 6.8865812249425815, 8.141487408542579, 9.764932926586592, 11.606365628342832), # 165
(10.286273093496636, 7.931529186743127, 11.178425815886285, 12.656851919932002, 12.728504063292343, 7.215112273624654, 6.468532122346058, 7.757558271707324, 13.968053248996117, 6.71036764548306, 7.935011820262847, 9.520357615514403, 11.322198105046873), # 166
(9.997353673783238, 7.6979683120316595, 10.882107170602728, 12.31349520927975, 12.389263501987168, 7.028385832305694, 6.28292435459308, 7.557997126749083, 13.61204254074304, 6.523974394132343, 7.716294106438124, 9.260780495496734, 11.019496615116793), # 167
(9.694451058192634, 7.454819235704206, 10.568639837073198, 11.951590838527274, 12.030699816489188, 6.830089863630398, 6.088963151990087, 7.345863117982976, 13.233028657172568, 6.328230756630195, 7.48632336001101, 8.987400973092019, 10.69971603772634), # 168
(9.378821445769624, 7.202999590535967, 10.239472774256495, 11.572741795265413, 11.654453892743392, 6.621180881409112, 5.887419346672787, 7.122190822163432, 12.832889642093342, 6.123966018716379, 7.24608867392411, 8.701418454858675, 10.364311252049257), # 169
(9.051721035559014, 6.94342700930214, 9.896054941111416, 11.178551067084992, 11.262166616694774, 6.402615399452171, 5.679063770776885, 6.888014816044876, 12.413503539313982, 5.912009466130653, 6.996579141120026, 8.404032347355134, 10.014737137259289), # 170
(8.7144060266056, 6.677019124777921, 9.539835296596765, 10.770621641576858, 10.85547887428833, 6.175349931569918, 5.464667256438089, 6.644369676381733, 11.976748392643131, 5.693190384612782, 6.738783854541357, 8.096442057139818, 9.652448572530185), # 171
(8.368132617954185, 6.4046935697385114, 9.172262799671339, 10.350556506331834, 10.436031551469046, 5.940340991572694, 5.245000635792105, 6.392289979928433, 11.524502245889417, 5.468338059902528, 6.473691907130711, 7.779846990771154, 9.278900437035686), # 172
(8.014157008649567, 6.127367976959108, 8.79478640929394, 9.919958648940762, 10.005465534181923, 5.69854509327084, 5.02083474097464, 6.132810303439398, 11.058643142861477, 5.238281777739651, 6.202292391830685, 7.45544655480756, 8.89554760994954), # 173
(7.6537353977365505, 5.845959979214909, 8.408855084423363, 9.480431056994465, 9.565421708371947, 5.450918750474696, 4.792940404121401, 5.866965223669057, 10.581049127367942, 5.003850823863915, 5.9255744015838845, 7.124440155807469, 8.503844970445494), # 174
(7.288123984259929, 5.561387209281111, 8.015917784018413, 9.033576718083788, 9.11754095998411, 5.198418476994606, 4.562088457368093, 5.595789317371834, 10.09359824321745, 4.765874484015079, 5.644527029332911, 6.788027200329303, 8.105247397697292), # 175
(6.91857896726451, 5.274567299932917, 7.617423467037885, 8.58099861979956, 8.663464174963408, 4.942000786640907, 4.329049732850424, 5.3203171613021585, 9.598168534218628, 4.525182043932907, 5.360139368020368, 6.447407094931487, 7.701209770878679), # 176
(6.546356545795092, 4.986417883945522, 7.214821092440582, 8.124299749732613, 8.204832239254838, 4.682622193223941, 4.094595062704101, 5.0415833322144525, 9.096638044180112, 4.282602789357159, 5.073400510588858, 6.103779246172446, 7.2931869691634), # 177
(6.172712918896475, 4.697856594094126, 6.809559619185302, 7.665083095473786, 7.743286038803382, 4.421239210554052, 3.859495279064828, 4.760622406863145, 8.590884816910537, 4.0389660060276, 4.78529954998098, 5.758343060610604, 6.882633871725203), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(10, 4, 10, 7, 4, 5, 6, 5, 3, 4, 0, 0, 0, 5, 15, 5, 4, 4, 3, 0, 2, 3, 4, 1, 0, 0), # 0
(12, 14, 21, 15, 9, 8, 9, 7, 8, 6, 0, 0, 0, 14, 25, 7, 9, 10, 7, 3, 7, 5, 12, 2, 0, 0), # 1
(13, 23, 27, 24, 17, 13, 14, 11, 9, 7, 2, 2, 0, 24, 29, 15, 20, 22, 12, 7, 9, 9, 16, 4, 4, 0), # 2
(20, 37, 32, 31, 23, 17, 14, 15, 12, 8, 5, 3, 0, 30, 37, 25, 25, 31, 19, 11, 12, 10, 16, 4, 6, 0), # 3
(31, 48, 45, 39, 30, 19, 19, 18, 15, 10, 9, 3, 0, 39, 40, 27, 34, 36, 24, 15, 15, 14, 18, 9, 6, 0), # 4
(41, 58, 50, 47, 39, 27, 22, 25, 21, 10, 14, 4, 0, 47, 56, 34, 37, 44, 29, 20, 16, 17, 23, 10, 6, 0), # 5
(54, 69, 58, 54, 47, 32, 30, 30, 28, 13, 15, 6, 0, 54, 65, 44, 46, 49, 37, 21, 17, 20, 24, 12, 7, 0), # 6
(68, 78, 65, 60, 53, 33, 35, 36, 35, 16, 17, 7, 0, 66, 77, 52, 48, 60, 43, 25, 18, 24, 25, 14, 8, 0), # 7
(80, 93, 72, 73, 63, 41, 39, 38, 37, 18, 19, 10, 0, 83, 87, 63, 57, 69, 44, 33, 20, 28, 31, 14, 10, 0), # 8
(86, 111, 81, 93, 71, 43, 47, 41, 39, 19, 19, 11, 0, 94, 101, 74, 66, 81, 53, 37, 23, 36, 38, 16, 11, 0), # 9
(104, 127, 94, 106, 77, 47, 54, 45, 46, 22, 20, 11, 0, 110, 108, 87, 72, 89, 61, 41, 26, 41, 44, 22, 12, 0), # 10
(117, 139, 107, 120, 86, 52, 58, 51, 53, 26, 22, 12, 0, 123, 124, 95, 80, 99, 69, 48, 31, 46, 46, 22, 14, 0), # 11
(132, 157, 120, 133, 98, 55, 66, 58, 56, 31, 25, 12, 0, 133, 137, 102, 91, 111, 79, 53, 35, 50, 48, 23, 17, 0), # 12
(144, 168, 130, 147, 105, 66, 71, 62, 62, 40, 27, 12, 0, 145, 150, 112, 99, 124, 90, 61, 38, 56, 51, 25, 18, 0), # 13
(159, 183, 151, 160, 117, 71, 77, 70, 67, 41, 30, 13, 0, 165, 161, 121, 106, 141, 97, 67, 45, 61, 56, 27, 19, 0), # 14
(178, 197, 161, 177, 127, 76, 86, 74, 77, 44, 31, 15, 0, 183, 180, 129, 116, 156, 100, 74, 50, 65, 57, 29, 20, 0), # 15
(189, 208, 174, 188, 138, 80, 87, 82, 84, 47, 33, 16, 0, 193, 196, 137, 122, 167, 110, 76, 54, 70, 67, 32, 20, 0), # 16
(199, 219, 182, 199, 149, 84, 92, 88, 88, 51, 36, 17, 0, 222, 210, 146, 127, 178, 113, 83, 59, 74, 73, 33, 21, 0), # 17
(212, 228, 195, 214, 154, 93, 100, 93, 93, 55, 36, 18, 0, 241, 228, 155, 136, 187, 125, 91, 65, 78, 76, 38, 22, 0), # 18
(234, 250, 209, 226, 170, 99, 109, 98, 102, 60, 38, 19, 0, 255, 248, 166, 154, 205, 127, 95, 70, 85, 78, 41, 24, 0), # 19
(248, 261, 222, 238, 182, 103, 111, 102, 108, 62, 40, 22, 0, 268, 264, 180, 159, 212, 137, 100, 74, 92, 80, 46, 25, 0), # 20
(261, 287, 239, 250, 201, 108, 116, 107, 114, 70, 44, 24, 0, 284, 280, 194, 172, 224, 145, 104, 83, 96, 87, 48, 30, 0), # 21
(269, 307, 254, 254, 211, 114, 122, 111, 124, 74, 44, 24, 0, 302, 291, 206, 179, 236, 153, 113, 85, 101, 93, 51, 31, 0), # 22
(286, 328, 271, 267, 226, 119, 132, 116, 132, 77, 46, 24, 0, 319, 302, 220, 183, 250, 161, 121, 87, 108, 95, 54, 33, 0), # 23
(309, 342, 280, 281, 240, 127, 139, 123, 138, 79, 48, 25, 0, 333, 324, 229, 193, 263, 169, 125, 91, 113, 104, 55, 33, 0), # 24
(329, 354, 292, 299, 249, 132, 151, 129, 146, 80, 51, 27, 0, 351, 338, 233, 198, 273, 182, 126, 96, 120, 111, 57, 34, 0), # 25
(344, 370, 303, 314, 261, 137, 152, 134, 155, 81, 51, 27, 0, 370, 354, 240, 211, 288, 192, 133, 103, 124, 112, 58, 34, 0), # 26
(367, 386, 314, 329, 276, 141, 159, 141, 160, 85, 54, 29, 0, 383, 372, 253, 228, 299, 201, 139, 106, 129, 115, 61, 35, 0), # 27
(386, 404, 331, 347, 288, 146, 162, 146, 169, 89, 56, 29, 0, 393, 383, 266, 239, 309, 210, 149, 109, 136, 121, 64, 37, 0), # 28
(402, 415, 347, 359, 300, 154, 170, 152, 174, 93, 59, 32, 0, 402, 393, 275, 255, 321, 223, 158, 112, 143, 129, 65, 40, 0), # 29
(414, 426, 361, 376, 313, 160, 176, 158, 179, 102, 64, 34, 0, 418, 412, 287, 268, 332, 230, 160, 118, 151, 132, 67, 42, 0), # 30
(428, 449, 378, 389, 333, 166, 182, 167, 185, 105, 65, 35, 0, 437, 428, 295, 276, 342, 238, 165, 122, 160, 136, 69, 43, 0), # 31
(446, 463, 391, 403, 348, 171, 186, 173, 190, 105, 67, 38, 0, 457, 445, 302, 287, 355, 245, 170, 126, 170, 137, 75, 46, 0), # 32
(466, 485, 409, 414, 358, 172, 196, 176, 194, 113, 68, 42, 0, 482, 462, 312, 294, 365, 250, 180, 129, 179, 142, 77, 47, 0), # 33
(487, 499, 420, 433, 369, 182, 200, 186, 203, 117, 69, 43, 0, 498, 472, 332, 309, 385, 264, 189, 132, 183, 150, 80, 48, 0), # 34
(503, 521, 437, 443, 382, 185, 205, 193, 209, 121, 73, 44, 0, 516, 490, 346, 319, 399, 273, 192, 135, 193, 154, 80, 48, 0), # 35
(514, 532, 447, 460, 389, 189, 216, 198, 217, 124, 77, 45, 0, 531, 503, 364, 330, 408, 275, 200, 142, 200, 157, 84, 48, 0), # 36
(524, 556, 460, 475, 402, 193, 219, 207, 222, 129, 80, 46, 0, 544, 519, 376, 337, 420, 285, 206, 153, 212, 161, 87, 50, 0), # 37
(543, 573, 469, 486, 411, 197, 227, 211, 226, 132, 83, 47, 0, 560, 539, 392, 351, 431, 297, 209, 153, 216, 169, 90, 52, 0), # 38
(555, 591, 487, 508, 422, 200, 232, 218, 233, 136, 85, 47, 0, 573, 563, 406, 361, 444, 309, 214, 158, 222, 173, 95, 54, 0), # 39
(570, 609, 500, 524, 435, 205, 238, 225, 237, 137, 88, 49, 0, 591, 581, 416, 372, 453, 315, 226, 159, 231, 179, 99, 56, 0), # 40
(588, 625, 509, 532, 446, 210, 247, 232, 243, 140, 91, 50, 0, 608, 595, 430, 381, 466, 321, 234, 163, 239, 185, 102, 58, 0), # 41
(602, 637, 519, 546, 455, 214, 255, 239, 247, 143, 93, 50, 0, 621, 608, 439, 394, 474, 337, 238, 166, 243, 187, 104, 59, 0), # 42
(626, 653, 532, 558, 475, 222, 262, 244, 252, 146, 94, 52, 0, 639, 620, 447, 402, 487, 345, 243, 172, 252, 192, 107, 61, 0), # 43
(642, 665, 544, 572, 493, 227, 271, 252, 255, 149, 95, 54, 0, 651, 637, 457, 410, 506, 356, 251, 178, 259, 195, 111, 63, 0), # 44
(653, 683, 558, 594, 506, 231, 282, 256, 263, 150, 98, 55, 0, 670, 650, 469, 424, 520, 359, 257, 183, 265, 199, 112, 66, 0), # 45
(674, 693, 572, 609, 513, 236, 288, 261, 273, 154, 98, 60, 0, 692, 664, 481, 434, 536, 368, 264, 186, 271, 205, 114, 67, 0), # 46
(688, 715, 586, 628, 528, 239, 297, 268, 278, 157, 98, 60, 0, 704, 685, 492, 442, 547, 374, 270, 195, 281, 207, 116, 69, 0), # 47
(716, 730, 594, 643, 542, 245, 302, 277, 290, 158, 98, 61, 0, 715, 698, 498, 451, 563, 377, 276, 198, 287, 207, 118, 72, 0), # 48
(726, 748, 611, 660, 556, 251, 308, 281, 295, 164, 99, 62, 0, 733, 713, 509, 461, 575, 384, 283, 202, 291, 210, 119, 72, 0), # 49
(739, 762, 619, 680, 569, 263, 314, 286, 298, 169, 104, 62, 0, 749, 727, 520, 466, 592, 391, 290, 207, 297, 215, 124, 73, 0), # 50
(758, 779, 641, 707, 585, 267, 316, 289, 305, 173, 105, 63, 0, 768, 737, 530, 475, 603, 401, 296, 209, 302, 221, 129, 74, 0), # 51
(770, 791, 660, 718, 594, 275, 323, 296, 313, 174, 106, 66, 0, 785, 755, 539, 481, 616, 409, 301, 213, 315, 224, 131, 75, 0), # 52
(780, 806, 673, 729, 607, 280, 328, 303, 317, 176, 107, 68, 0, 800, 770, 545, 487, 632, 413, 306, 219, 321, 228, 132, 78, 0), # 53
(804, 826, 689, 742, 612, 290, 336, 314, 319, 177, 109, 68, 0, 819, 788, 558, 494, 649, 424, 313, 224, 325, 231, 132, 78, 0), # 54
(822, 844, 703, 759, 623, 296, 342, 317, 324, 181, 109, 68, 0, 830, 807, 568, 499, 662, 434, 318, 228, 331, 236, 133, 80, 0), # 55
(842, 857, 717, 779, 638, 303, 349, 319, 331, 188, 112, 68, 0, 846, 826, 579, 505, 677, 445, 322, 232, 338, 244, 137, 81, 0), # 56
(858, 870, 737, 789, 644, 311, 352, 325, 338, 189, 113, 69, 0, 865, 843, 589, 514, 691, 449, 328, 237, 347, 250, 139, 83, 0), # 57
(877, 885, 747, 809, 652, 318, 358, 334, 341, 196, 116, 69, 0, 883, 858, 595, 522, 704, 454, 335, 241, 352, 253, 146, 87, 0), # 58
(891, 895, 760, 829, 667, 323, 362, 340, 343, 197, 118, 69, 0, 903, 871, 609, 531, 720, 461, 341, 242, 361, 255, 148, 89, 0), # 59
(904, 909, 767, 844, 676, 329, 367, 345, 346, 199, 120, 72, 0, 920, 887, 620, 533, 734, 466, 351, 246, 367, 261, 149, 90, 0), # 60
(920, 925, 780, 857, 687, 338, 373, 347, 354, 202, 122, 72, 0, 938, 896, 627, 545, 752, 472, 357, 249, 373, 266, 153, 92, 0), # 61
(935, 940, 795, 863, 697, 346, 378, 351, 359, 204, 124, 75, 0, 959, 910, 634, 553, 767, 486, 362, 252, 377, 274, 156, 94, 0), # 62
(951, 948, 818, 880, 707, 351, 382, 363, 368, 206, 126, 75, 0, 972, 931, 649, 562, 786, 490, 370, 259, 380, 279, 159, 95, 0), # 63
(969, 961, 840, 890, 713, 356, 386, 367, 371, 210, 128, 77, 0, 988, 947, 658, 567, 798, 496, 377, 265, 387, 285, 162, 96, 0), # 64
(984, 975, 855, 904, 729, 362, 395, 371, 376, 212, 134, 79, 0, 1004, 956, 678, 574, 812, 504, 382, 269, 394, 293, 164, 97, 0), # 65
(1003, 989, 877, 919, 743, 369, 402, 377, 384, 216, 137, 80, 0, 1025, 976, 689, 582, 825, 508, 390, 274, 399, 298, 168, 97, 0), # 66
(1019, 1006, 890, 934, 752, 375, 405, 385, 390, 217, 143, 80, 0, 1041, 997, 705, 593, 838, 514, 400, 279, 407, 301, 169, 99, 0), # 67
(1039, 1019, 903, 951, 764, 379, 412, 394, 396, 221, 147, 86, 0, 1058, 1011, 712, 600, 848, 524, 406, 281, 414, 303, 173, 100, 0), # 68
(1054, 1040, 916, 969, 775, 389, 417, 397, 401, 225, 149, 88, 0, 1074, 1023, 719, 615, 863, 528, 416, 284, 419, 307, 177, 101, 0), # 69
(1069, 1053, 933, 977, 784, 396, 422, 399, 407, 226, 154, 88, 0, 1094, 1032, 733, 618, 880, 534, 426, 292, 422, 313, 178, 101, 0), # 70
(1081, 1069, 946, 994, 799, 407, 433, 403, 418, 232, 156, 88, 0, 1109, 1046, 740, 629, 890, 534, 433, 296, 428, 315, 181, 102, 0), # 71
(1101, 1083, 958, 1009, 812, 411, 441, 408, 421, 236, 157, 91, 0, 1123, 1062, 755, 642, 897, 540, 445, 304, 438, 325, 184, 102, 0), # 72
(1118, 1099, 974, 1024, 828, 419, 447, 412, 425, 239, 161, 92, 0, 1144, 1078, 767, 656, 916, 547, 448, 311, 444, 330, 184, 103, 0), # 73
(1135, 1111, 989, 1036, 840, 425, 453, 418, 431, 241, 163, 92, 0, 1156, 1095, 777, 665, 931, 552, 450, 318, 452, 338, 187, 105, 0), # 74
(1148, 1121, 1003, 1052, 852, 435, 460, 420, 439, 243, 166, 93, 0, 1176, 1103, 788, 676, 945, 556, 457, 321, 457, 339, 191, 105, 0), # 75
(1167, 1143, 1013, 1069, 859, 441, 470, 425, 445, 245, 170, 94, 0, 1187, 1119, 799, 680, 958, 563, 465, 324, 467, 340, 196, 108, 0), # 76
(1176, 1159, 1031, 1077, 866, 449, 474, 430, 447, 247, 175, 96, 0, 1206, 1135, 813, 690, 975, 570, 474, 326, 471, 345, 198, 108, 0), # 77
(1192, 1180, 1044, 1084, 879, 458, 482, 434, 451, 249, 180, 96, 0, 1221, 1155, 826, 695, 986, 586, 481, 329, 476, 351, 201, 109, 0), # 78
(1210, 1190, 1058, 1095, 889, 466, 493, 437, 463, 250, 182, 98, 0, 1237, 1167, 836, 703, 995, 594, 488, 333, 483, 355, 203, 110, 0), # 79
(1224, 1209, 1067, 1111, 908, 473, 499, 445, 471, 251, 187, 99, 0, 1250, 1181, 841, 708, 1006, 600, 491, 338, 489, 361, 205, 113, 0), # 80
(1235, 1222, 1077, 1121, 926, 477, 503, 447, 474, 252, 188, 100, 0, 1263, 1183, 854, 715, 1017, 604, 497, 341, 492, 365, 209, 116, 0), # 81
(1251, 1239, 1088, 1133, 937, 483, 509, 451, 481, 254, 193, 100, 0, 1278, 1197, 868, 734, 1031, 606, 502, 342, 499, 368, 213, 117, 0), # 82
(1266, 1252, 1103, 1156, 947, 489, 512, 455, 484, 254, 194, 103, 0, 1292, 1215, 879, 742, 1042, 617, 508, 343, 509, 371, 215, 117, 0), # 83
(1283, 1263, 1117, 1173, 963, 493, 517, 459, 489, 257, 195, 103, 0, 1309, 1230, 892, 748, 1051, 622, 510, 344, 513, 377, 216, 117, 0), # 84
(1301, 1277, 1133, 1184, 973, 497, 521, 463, 495, 259, 197, 104, 0, 1323, 1241, 901, 752, 1060, 630, 517, 347, 522, 379, 221, 117, 0), # 85
(1314, 1296, 1144, 1199, 982, 503, 529, 467, 500, 263, 198, 108, 0, 1334, 1260, 909, 757, 1073, 634, 525, 354, 531, 381, 224, 119, 0), # 86
(1324, 1307, 1157, 1216, 994, 508, 538, 471, 510, 267, 201, 108, 0, 1351, 1275, 919, 766, 1082, 644, 532, 357, 539, 387, 225, 120, 0), # 87
(1342, 1319, 1172, 1232, 1001, 513, 539, 473, 518, 269, 204, 112, 0, 1369, 1293, 928, 769, 1100, 652, 540, 358, 547, 390, 230, 120, 0), # 88
(1363, 1334, 1189, 1246, 1015, 518, 544, 481, 524, 271, 204, 114, 0, 1384, 1305, 936, 781, 1115, 655, 545, 360, 552, 394, 234, 122, 0), # 89
(1381, 1347, 1204, 1255, 1025, 526, 548, 488, 529, 275, 207, 114, 0, 1398, 1313, 947, 786, 1126, 664, 549, 362, 556, 402, 239, 124, 0), # 90
(1397, 1359, 1216, 1275, 1035, 532, 554, 492, 530, 277, 209, 116, 0, 1415, 1324, 952, 795, 1138, 672, 554, 365, 560, 407, 240, 124, 0), # 91
(1419, 1375, 1225, 1289, 1042, 536, 559, 496, 540, 280, 211, 117, 0, 1427, 1333, 962, 807, 1149, 682, 557, 369, 563, 412, 242, 125, 0), # 92
(1438, 1388, 1245, 1300, 1054, 542, 567, 504, 552, 281, 212, 117, 0, 1445, 1350, 968, 810, 1160, 687, 564, 371, 572, 418, 244, 127, 0), # 93
(1457, 1403, 1256, 1321, 1062, 549, 571, 505, 558, 283, 214, 117, 0, 1466, 1364, 978, 816, 1181, 693, 571, 374, 578, 421, 248, 127, 0), # 94
(1472, 1418, 1268, 1345, 1076, 555, 579, 508, 568, 286, 217, 119, 0, 1490, 1376, 989, 823, 1191, 700, 578, 379, 584, 425, 250, 127, 0), # 95
(1485, 1433, 1282, 1354, 1094, 563, 583, 512, 574, 293, 219, 119, 0, 1504, 1387, 994, 832, 1207, 711, 583, 385, 589, 429, 251, 130, 0), # 96
(1497, 1440, 1297, 1368, 1112, 568, 587, 518, 581, 295, 225, 122, 0, 1523, 1403, 1000, 838, 1226, 713, 590, 389, 593, 437, 253, 132, 0), # 97
(1508, 1448, 1305, 1377, 1121, 573, 590, 522, 587, 297, 227, 123, 0, 1539, 1422, 1012, 843, 1233, 724, 595, 395, 599, 440, 255, 133, 0), # 98
(1518, 1461, 1316, 1390, 1135, 577, 594, 524, 597, 300, 228, 124, 0, 1555, 1436, 1021, 849, 1246, 727, 597, 398, 605, 445, 255, 133, 0), # 99
(1538, 1472, 1326, 1404, 1147, 582, 601, 527, 604, 303, 232, 125, 0, 1567, 1446, 1033, 850, 1261, 731, 605, 404, 610, 452, 256, 135, 0), # 100
(1549, 1482, 1337, 1414, 1162, 587, 606, 530, 611, 304, 233, 125, 0, 1583, 1458, 1041, 860, 1276, 740, 609, 409, 612, 456, 262, 137, 0), # 101
(1561, 1494, 1350, 1425, 1173, 596, 612, 536, 617, 306, 236, 126, 0, 1600, 1470, 1050, 869, 1291, 746, 614, 414, 624, 462, 263, 137, 0), # 102
(1577, 1512, 1362, 1443, 1181, 603, 614, 540, 626, 307, 237, 131, 0, 1615, 1487, 1063, 873, 1307, 748, 620, 418, 628, 465, 266, 140, 0), # 103
(1594, 1523, 1374, 1454, 1194, 611, 620, 543, 631, 312, 240, 132, 0, 1634, 1492, 1072, 879, 1319, 757, 625, 423, 631, 475, 267, 140, 0), # 104
(1607, 1538, 1386, 1466, 1205, 620, 624, 546, 635, 313, 244, 133, 0, 1650, 1509, 1084, 881, 1335, 763, 631, 426, 637, 482, 269, 140, 0), # 105
(1618, 1551, 1398, 1480, 1212, 629, 631, 554, 642, 314, 246, 134, 0, 1666, 1520, 1092, 886, 1345, 770, 635, 430, 644, 483, 271, 141, 0), # 106
(1629, 1559, 1409, 1497, 1228, 634, 637, 557, 649, 315, 248, 136, 0, 1677, 1533, 1098, 890, 1358, 776, 638, 432, 650, 493, 272, 142, 0), # 107
(1646, 1569, 1418, 1512, 1243, 638, 642, 561, 661, 319, 250, 137, 0, 1689, 1547, 1107, 897, 1374, 779, 644, 436, 654, 494, 278, 143, 0), # 108
(1672, 1580, 1432, 1533, 1256, 643, 647, 566, 664, 320, 255, 138, 0, 1706, 1557, 1114, 903, 1382, 787, 652, 439, 659, 496, 280, 144, 0), # 109
(1686, 1594, 1453, 1553, 1277, 648, 648, 570, 666, 322, 256, 141, 0, 1718, 1571, 1126, 912, 1397, 790, 657, 442, 664, 501, 283, 144, 0), # 110
(1705, 1610, 1472, 1567, 1291, 656, 650, 571, 673, 324, 258, 144, 0, 1738, 1581, 1133, 921, 1408, 796, 661, 446, 667, 506, 284, 145, 0), # 111
(1725, 1622, 1482, 1576, 1306, 658, 656, 575, 681, 329, 260, 145, 0, 1758, 1589, 1140, 929, 1419, 804, 666, 449, 673, 514, 286, 147, 0), # 112
(1736, 1635, 1499, 1590, 1322, 667, 662, 579, 688, 332, 262, 145, 0, 1772, 1605, 1150, 935, 1430, 811, 670, 451, 679, 516, 288, 149, 0), # 113
(1749, 1648, 1512, 1600, 1335, 670, 665, 580, 692, 335, 267, 146, 0, 1782, 1620, 1156, 944, 1441, 812, 677, 454, 685, 519, 290, 149, 0), # 114
(1759, 1662, 1529, 1612, 1349, 678, 670, 582, 697, 339, 268, 147, 0, 1799, 1630, 1173, 953, 1450, 822, 680, 459, 693, 522, 292, 149, 0), # 115
(1773, 1669, 1543, 1626, 1366, 686, 675, 588, 700, 342, 269, 147, 0, 1809, 1641, 1183, 961, 1465, 828, 685, 466, 702, 528, 295, 150, 0), # 116
(1784, 1684, 1558, 1638, 1379, 691, 678, 593, 707, 344, 270, 148, 0, 1827, 1649, 1198, 972, 1472, 836, 686, 470, 708, 533, 297, 151, 0), # 117
(1801, 1690, 1573, 1649, 1391, 696, 683, 595, 712, 349, 272, 149, 0, 1851, 1656, 1208, 978, 1488, 838, 692, 479, 711, 537, 298, 152, 0), # 118
(1818, 1706, 1590, 1662, 1398, 701, 687, 602, 716, 349, 275, 150, 0, 1866, 1666, 1213, 985, 1497, 839, 695, 484, 715, 540, 301, 153, 0), # 119
(1829, 1715, 1604, 1676, 1413, 704, 690, 604, 722, 351, 276, 150, 0, 1883, 1677, 1221, 994, 1509, 848, 696, 485, 719, 546, 302, 155, 0), # 120
(1841, 1727, 1618, 1683, 1427, 709, 693, 606, 729, 353, 278, 151, 0, 1895, 1688, 1233, 1001, 1515, 852, 698, 485, 724, 551, 303, 155, 0), # 121
(1852, 1736, 1632, 1697, 1442, 718, 702, 609, 735, 356, 278, 152, 0, 1908, 1695, 1237, 1007, 1529, 858, 708, 489, 729, 557, 303, 155, 0), # 122
(1860, 1750, 1651, 1712, 1459, 720, 703, 613, 740, 358, 280, 153, 0, 1926, 1706, 1250, 1018, 1535, 868, 711, 493, 737, 559, 305, 155, 0), # 123
(1874, 1758, 1663, 1728, 1467, 722, 708, 615, 746, 362, 281, 153, 0, 1948, 1717, 1257, 1026, 1547, 877, 716, 497, 743, 559, 306, 155, 0), # 124
(1891, 1771, 1673, 1746, 1477, 726, 717, 618, 751, 365, 281, 157, 0, 1971, 1729, 1267, 1033, 1567, 886, 719, 504, 750, 565, 307, 155, 0), # 125
(1903, 1780, 1682, 1753, 1487, 728, 723, 621, 757, 366, 283, 158, 0, 1980, 1745, 1271, 1043, 1580, 888, 720, 509, 756, 567, 310, 155, 0), # 126
(1917, 1791, 1688, 1764, 1502, 731, 728, 624, 765, 367, 285, 160, 0, 1992, 1754, 1279, 1050, 1585, 895, 729, 514, 763, 571, 314, 156, 0), # 127
(1930, 1802, 1701, 1776, 1516, 737, 733, 629, 768, 369, 286, 161, 0, 2012, 1766, 1291, 1056, 1596, 908, 732, 520, 771, 572, 319, 158, 0), # 128
(1948, 1809, 1713, 1788, 1529, 739, 736, 634, 775, 369, 287, 163, 0, 2027, 1777, 1304, 1068, 1611, 914, 738, 521, 777, 576, 322, 159, 0), # 129
(1956, 1817, 1725, 1805, 1540, 742, 743, 636, 777, 372, 287, 163, 0, 2037, 1784, 1313, 1078, 1625, 920, 739, 524, 782, 580, 324, 160, 0), # 130
(1969, 1827, 1742, 1815, 1549, 747, 747, 637, 781, 375, 289, 165, 0, 2051, 1794, 1321, 1089, 1640, 927, 745, 527, 787, 582, 324, 160, 0), # 131
(1991, 1833, 1755, 1825, 1560, 751, 756, 642, 791, 377, 292, 165, 0, 2062, 1809, 1325, 1096, 1652, 940, 749, 529, 790, 587, 326, 162, 0), # 132
(1999, 1851, 1770, 1836, 1573, 757, 760, 648, 802, 380, 292, 168, 0, 2075, 1822, 1335, 1107, 1659, 947, 752, 534, 794, 589, 327, 162, 0), # 133
(2013, 1868, 1780, 1844, 1584, 759, 768, 653, 809, 382, 295, 171, 0, 2088, 1830, 1346, 1115, 1670, 951, 754, 537, 797, 592, 327, 163, 0), # 134
(2020, 1878, 1791, 1858, 1591, 774, 775, 656, 812, 384, 295, 171, 0, 2103, 1836, 1356, 1119, 1679, 954, 757, 543, 803, 598, 328, 164, 0), # 135
(2033, 1889, 1800, 1864, 1601, 776, 775, 660, 816, 388, 297, 171, 0, 2118, 1845, 1366, 1126, 1690, 957, 762, 544, 813, 599, 330, 164, 0), # 136
(2048, 1896, 1811, 1873, 1611, 778, 777, 663, 820, 392, 298, 171, 0, 2132, 1859, 1371, 1129, 1701, 963, 768, 548, 817, 604, 332, 166, 0), # 137
(2054, 1907, 1825, 1888, 1620, 785, 781, 665, 820, 392, 301, 171, 0, 2139, 1871, 1379, 1137, 1713, 966, 773, 552, 821, 605, 333, 167, 0), # 138
(2064, 1915, 1838, 1895, 1631, 788, 785, 672, 825, 394, 304, 171, 0, 2157, 1884, 1383, 1145, 1721, 969, 776, 555, 824, 611, 338, 169, 0), # 139
(2078, 1931, 1851, 1907, 1644, 791, 785, 675, 831, 395, 306, 172, 0, 2167, 1891, 1388, 1152, 1729, 974, 777, 558, 832, 616, 339, 170, 0), # 140
(2092, 1943, 1869, 1917, 1656, 795, 787, 677, 838, 395, 307, 172, 0, 2178, 1895, 1400, 1159, 1744, 980, 782, 559, 836, 621, 343, 172, 0), # 141
(2103, 1952, 1883, 1929, 1662, 801, 789, 681, 846, 397, 311, 172, 0, 2195, 1907, 1412, 1164, 1753, 988, 787, 561, 842, 630, 347, 172, 0), # 142
(2118, 1961, 1893, 1945, 1669, 807, 796, 683, 848, 400, 313, 173, 0, 2215, 1918, 1425, 1173, 1765, 995, 788, 571, 852, 633, 349, 174, 0), # 143
(2135, 1972, 1900, 1961, 1683, 813, 800, 685, 854, 400, 314, 175, 0, 2229, 1930, 1434, 1180, 1782, 1000, 792, 573, 856, 638, 352, 174, 0), # 144
(2147, 1983, 1912, 1974, 1691, 818, 805, 690, 860, 402, 315, 175, 0, 2243, 1941, 1440, 1189, 1793, 1007, 796, 577, 862, 643, 355, 175, 0), # 145
(2156, 1990, 1921, 1981, 1697, 823, 809, 693, 866, 404, 316, 175, 0, 2263, 1949, 1451, 1201, 1803, 1012, 800, 582, 868, 647, 356, 176, 0), # 146
(2170, 1999, 1931, 1994, 1705, 824, 813, 695, 873, 408, 317, 175, 0, 2280, 1964, 1457, 1207, 1815, 1017, 803, 586, 873, 650, 359, 176, 0), # 147
(2182, 2003, 1939, 2010, 1715, 828, 818, 699, 878, 409, 318, 176, 0, 2291, 1971, 1464, 1213, 1828, 1020, 805, 589, 876, 653, 361, 178, 0), # 148
(2195, 2014, 1956, 2022, 1723, 831, 823, 702, 882, 412, 322, 178, 0, 2307, 1979, 1475, 1217, 1841, 1024, 809, 592, 881, 656, 363, 179, 0), # 149
(2205, 2021, 1975, 2032, 1731, 835, 830, 706, 888, 413, 325, 178, 0, 2320, 1989, 1485, 1225, 1853, 1029, 812, 596, 890, 659, 365, 179, 0), # 150
(2221, 2031, 1982, 2043, 1742, 838, 835, 715, 891, 415, 327, 178, 0, 2336, 1994, 1494, 1231, 1862, 1031, 813, 597, 895, 665, 371, 179, 0), # 151
(2232, 2043, 1988, 2055, 1751, 840, 838, 720, 896, 417, 328, 180, 0, 2343, 2003, 1497, 1235, 1874, 1037, 815, 601, 902, 670, 376, 179, 0), # 152
(2243, 2049, 1998, 2064, 1759, 843, 841, 723, 903, 421, 329, 180, 0, 2354, 2017, 1499, 1246, 1886, 1041, 819, 609, 907, 674, 377, 179, 0), # 153
(2257, 2060, 2005, 2076, 1771, 848, 848, 731, 910, 424, 331, 181, 0, 2363, 2032, 1513, 1250, 1888, 1048, 823, 615, 908, 679, 383, 181, 0), # 154
(2268, 2069, 2015, 2085, 1785, 856, 853, 734, 913, 425, 332, 182, 0, 2378, 2043, 1516, 1256, 1898, 1052, 829, 618, 913, 685, 385, 181, 0), # 155
(2282, 2076, 2029, 2100, 1793, 865, 858, 736, 918, 426, 336, 184, 0, 2385, 2053, 1524, 1259, 1908, 1057, 834, 622, 914, 690, 385, 182, 0), # 156
(2292, 2087, 2042, 2114, 1799, 878, 861, 737, 925, 429, 336, 186, 0, 2400, 2063, 1537, 1264, 1926, 1062, 838, 624, 921, 694, 393, 183, 0), # 157
(2306, 2096, 2049, 2126, 1806, 885, 864, 741, 932, 432, 339, 187, 0, 2416, 2074, 1542, 1268, 1937, 1070, 840, 627, 926, 703, 395, 183, 0), # 158
(2317, 2106, 2058, 2139, 1820, 889, 867, 743, 934, 437, 339, 187, 0, 2435, 2080, 1547, 1273, 1947, 1075, 843, 629, 932, 710, 399, 185, 0), # 159
(2325, 2112, 2069, 2149, 1833, 896, 870, 748, 940, 441, 339, 188, 0, 2448, 2090, 1549, 1276, 1955, 1083, 848, 633, 938, 712, 405, 185, 0), # 160
(2331, 2120, 2084, 2161, 1842, 904, 873, 751, 948, 443, 340, 189, 0, 2463, 2097, 1560, 1279, 1969, 1089, 851, 637, 944, 713, 408, 186, 0), # 161
(2343, 2129, 2098, 2166, 1852, 906, 876, 757, 950, 445, 341, 190, 0, 2468, 2110, 1571, 1281, 1978, 1093, 855, 638, 949, 715, 408, 186, 0), # 162
(2351, 2136, 2106, 2184, 1857, 910, 879, 762, 951, 446, 341, 192, 0, 2482, 2116, 1580, 1284, 1986, 1098, 858, 641, 952, 717, 411, 187, 0), # 163
(2364, 2147, 2113, 2191, 1867, 913, 884, 767, 956, 447, 342, 192, 0, 2485, 2124, 1587, 1289, 1997, 1101, 859, 644, 958, 723, 412, 188, 0), # 164
(2380, 2163, 2119, 2206, 1874, 916, 886, 770, 959, 448, 342, 195, 0, 2499, 2143, 1594, 1296, 2007, 1104, 860, 649, 961, 724, 413, 190, 0), # 165
(2393, 2169, 2127, 2219, 1890, 918, 890, 779, 963, 448, 343, 195, 0, 2507, 2151, 1598, 1300, 2014, 1109, 862, 654, 966, 729, 414, 190, 0), # 166
(2403, 2179, 2136, 2225, 1897, 920, 891, 781, 968, 450, 346, 196, 0, 2517, 2163, 1610, 1306, 2017, 1113, 866, 659, 971, 730, 415, 190, 0), # 167
(2414, 2185, 2142, 2235, 1909, 922, 892, 785, 974, 452, 348, 198, 0, 2531, 2176, 1617, 1313, 2027, 1115, 869, 664, 978, 734, 415, 190, 0), # 168
(2431, 2188, 2147, 2244, 1916, 926, 894, 787, 979, 454, 348, 198, 0, 2547, 2185, 1624, 1318, 2038, 1119, 869, 668, 983, 741, 415, 190, 0), # 169
(2441, 2196, 2155, 2255, 1922, 930, 896, 790, 984, 457, 349, 199, 0, 2555, 2195, 1627, 1322, 2050, 1122, 874, 670, 984, 744, 417, 190, 0), # 170
(2447, 2200, 2159, 2259, 1927, 934, 899, 792, 986, 457, 349, 200, 0, 2562, 2201, 1633, 1324, 2059, 1126, 876, 674, 989, 747, 418, 190, 0), # 171
(2455, 2206, 2173, 2262, 1935, 940, 901, 794, 991, 457, 350, 201, 0, 2574, 2206, 1637, 1327, 2069, 1127, 878, 676, 989, 752, 420, 191, 0), # 172
(2467, 2213, 2184, 2270, 1941, 946, 905, 795, 995, 458, 350, 201, 0, 2583, 2216, 1642, 1329, 2080, 1130, 881, 678, 994, 754, 420, 191, 0), # 173
(2473, 2219, 2193, 2275, 1946, 949, 908, 795, 998, 460, 350, 201, 0, 2592, 2225, 1644, 1331, 2091, 1132, 887, 682, 1001, 756, 422, 192, 0), # 174
(2477, 2225, 2199, 2279, 1955, 952, 911, 796, 1004, 461, 351, 201, 0, 2599, 2232, 1651, 1335, 2100, 1138, 887, 684, 1005, 758, 424, 193, 0), # 175
(2484, 2229, 2209, 2285, 1959, 955, 913, 797, 1008, 461, 351, 201, 0, 2606, 2247, 1656, 1337, 2106, 1140, 891, 685, 1009, 761, 428, 193, 0), # 176
(2491, 2230, 2211, 2289, 1963, 955, 915, 799, 1010, 461, 351, 201, 0, 2615, 2253, 1658, 1343, 2109, 1143, 893, 688, 1011, 761, 430, 193, 0), # 177
(2494, 2232, 2216, 2292, 1966, 955, 920, 802, 1014, 461, 353, 201, 0, 2618, 2257, 1663, 1349, 2113, 1146, 898, 689, 1013, 762, 435, 194, 0), # 178
(2494, 2232, 2216, 2292, 1966, 955, 920, 802, 1014, 461, 353, 201, 0, 2618, 2257, 1663, 1349, 2113, 1146, 898, 689, 1013, 762, 435, 194, 0), # 179
)
passenger_arriving_rate = (
(8.033384925394829, 8.103756554216645, 6.9483776394833425, 7.45760132863612, 5.924997981450252, 2.9294112699015167, 3.3168284922991322, 3.102117448652949, 3.2480528331562706, 1.5832060062089484, 1.1214040437028276, 0.6530553437741565, 0.0, 8.134208340125381, 7.183608781515721, 5.607020218514138, 4.749618018626844, 6.496105666312541, 4.342964428114128, 3.3168284922991322, 2.0924366213582264, 2.962498990725126, 2.4858671095453735, 1.3896755278966686, 0.7367051412924223, 0.0), # 0
(8.566923443231959, 8.638755684745645, 7.407128788440204, 7.95017310393194, 6.317323026639185, 3.122918011773052, 3.535575153010955, 3.306342481937139, 3.462530840710885, 1.6875922769108604, 1.1954923029216353, 0.6961622214419141, 0.0, 8.671666635903767, 7.657784435861053, 5.9774615146081755, 5.06277683073258, 6.92506168142177, 4.628879474711995, 3.535575153010955, 2.230655722695037, 3.1586615133195926, 2.650057701310647, 1.4814257576880407, 0.7853414258859679, 0.0), # 1
(9.09875681436757, 9.171631583973436, 7.864056380729885, 8.440785245597754, 6.708227171999727, 3.3156527735449486, 3.7534548063685635, 3.5097501652696135, 3.676152963668026, 1.7915655100082188, 1.269286173007017, 0.7390976869404075, 0.0, 9.206983725135505, 8.13007455634448, 6.346430865035084, 5.374696530024655, 7.352305927336052, 4.913650231377459, 3.7534548063685635, 2.3683234096749635, 3.3541135859998636, 2.8135950818659183, 1.5728112761459772, 0.8337846894521307, 0.0), # 2
(9.6268124690345, 9.70027006950679, 8.317347825759807, 8.927491689038488, 7.096172454402028, 3.5068512477461056, 3.9696029133183646, 3.7115341049963386, 3.8880720858245827, 1.8947130793704727, 1.3424929098206355, 0.7816914246573948, 0.0, 9.738036490006762, 8.598605671231342, 6.712464549103178, 5.684139238111417, 7.7761441716491655, 5.196147746994874, 3.9696029133183646, 2.5048937483900753, 3.548086227201014, 2.97583056301283, 1.6634695651519613, 0.8818427335915264, 0.0), # 3
(10.149017837465571, 10.222556958952469, 8.765190532937382, 9.408346369659084, 7.479620910716259, 3.6957491269054237, 4.183154934806767, 3.910887907463277, 4.097441090977444, 1.996622358867072, 1.4148197692241535, 0.8237731189806353, 0.0, 10.262701812703709, 9.061504308786986, 7.074098846120767, 5.9898670766012145, 8.194882181954888, 5.475243070448588, 4.183154934806767, 2.6398208049324454, 3.7398104553581293, 3.136115456553029, 1.7530381065874767, 0.9293233599047701, 0.0), # 4
(10.663300349893618, 10.736378069917262, 9.205771911670025, 9.881403222864472, 7.8570345778125645, 3.8815821035518008, 4.393246331780179, 4.1070051790163955, 4.303412862923498, 2.096880722367466, 1.4859740070792353, 0.8651724542978865, 0.0, 10.778856575412524, 9.51689699727675, 7.429870035396177, 6.290642167102396, 8.606825725846996, 5.749807250622953, 4.393246331780179, 2.772558645394143, 3.9285172889062823, 3.2938010742881585, 1.841154382334005, 0.9760343699924785, 0.0), # 5
(11.167587436551466, 11.239619220007935, 9.637279371365155, 10.344716184059584, 8.226875492561113, 4.06358587021414, 4.59901256518501, 4.299079526001659, 4.5051402854596345, 2.195075543741104, 1.555662879247542, 0.9057191149969079, 0.0, 11.284377660319372, 9.962910264965986, 7.77831439623771, 6.5852266312233105, 9.010280570919269, 6.018711336402323, 4.59901256518501, 2.902561335867243, 4.113437746280557, 3.448238728019862, 1.9274558742730312, 1.021783565455267, 0.0), # 6
(11.65980652767195, 11.73016622683126, 10.05790032143018, 10.796339188649355, 8.587605691832056, 4.2409961194213395, 4.799589095967668, 4.486304554765035, 4.701776242382744, 2.2907941968574352, 1.6235936415907386, 0.9452427854654573, 0.0, 11.777141949610431, 10.397670640120028, 8.117968207953693, 6.872382590572304, 9.403552484765488, 6.280826376671049, 4.799589095967668, 3.029282942443814, 4.293802845916028, 3.598779729549786, 2.0115800642860364, 1.066378747893751, 0.0), # 7
(12.137885053487896, 12.205904907994013, 10.465822171272528, 11.234326172038713, 8.937687212495558, 4.413048543702297, 4.994111385074558, 4.667873871652484, 4.89247361748971, 2.3836240555859103, 1.6894735499704858, 0.9835731500912939, 0.0, 12.255026325471867, 10.81930465100423, 8.447367749852429, 7.150872166757729, 9.78494723497942, 6.535023420313477, 4.994111385074558, 3.152177531215927, 4.468843606247779, 3.744775390679572, 2.093164434254506, 1.1096277189085468, 0.0), # 8
(12.599750444232136, 12.664721081102966, 10.859232330299607, 11.656731069632603, 9.27558209142177, 4.578978835585919, 5.181714893452096, 4.842981083009976, 5.076385294577426, 2.4731524937959772, 1.7530098602484476, 1.0205398932621754, 0.0, 12.71590767008986, 11.225938825883926, 8.765049301242238, 7.41945748138793, 10.152770589154851, 6.780173516213966, 5.181714893452096, 3.270699168275656, 4.637791045710885, 3.8855770232108684, 2.1718464660599213, 1.1513382801002698, 0.0), # 9
(13.043330130137491, 13.104500563764889, 11.236318207918833, 12.061607816835945, 9.599752365480853, 4.7380226876011005, 5.361535082046684, 5.010819795183474, 5.252664157442781, 2.558966885357086, 1.8139098282862867, 1.0559726993658605, 0.0, 13.157662865650577, 11.615699693024464, 9.069549141431432, 7.676900656071258, 10.505328314885562, 7.015147713256865, 5.361535082046684, 3.3843019197150714, 4.799876182740427, 4.020535938945316, 2.247263641583767, 1.1913182330695355, 0.0), # 10
(13.466551541436809, 13.52312917358657, 11.595267213537621, 12.447010349053677, 9.908660071542968, 4.889415792276744, 5.532707411804733, 5.170583614518944, 5.420463089882663, 2.640654604138688, 1.8718807099456667, 1.0897012527901082, 0.0, 13.57816879434018, 11.986713780691188, 9.359403549728333, 7.921963812416063, 10.840926179765326, 7.238817060326522, 5.532707411804733, 3.4924398516262456, 4.954330035771484, 4.14900344968456, 2.3190534427075247, 1.229375379416961, 0.0), # 11
(13.8673421083629, 13.918492728174757, 11.934266756563387, 12.810992601690735, 10.200767246478268, 5.032393842141746, 5.694367343672649, 5.321466147362347, 5.578934975693962, 2.7178030240102293, 1.9266297610882495, 1.1215552379226759, 0.0, 13.975302338344855, 12.337107617149433, 9.633148805441246, 8.153409072030687, 11.157869951387925, 7.4500526063072865, 5.694367343672649, 3.5945670301012465, 5.100383623239134, 4.270330867230246, 2.3868533513126775, 1.26531752074316, 0.0), # 12
(14.243629261148602, 14.288477045136244, 12.251504246403549, 13.151608510152053, 10.474535927156907, 5.166192529725009, 5.845650338596845, 5.462661000059654, 5.727232698673564, 2.7899995188411624, 1.9778642375756985, 1.1513643391513229, 0.0, 14.346940379850777, 12.66500773066455, 9.889321187878492, 8.369998556523486, 11.454465397347128, 7.647725400083517, 5.845650338596845, 3.6901375212321494, 5.237267963578454, 4.383869503384019, 2.45030084928071, 1.2989524586487495, 0.0), # 13
(14.593340430026746, 14.630967942077797, 12.54516709246553, 13.466912009842552, 10.728428150449055, 5.2900475475554325, 5.9856918575237295, 5.593361778956831, 5.864509142618358, 2.856831462500934, 2.0252913952696763, 1.1789582408638082, 0.0, 14.690959801044102, 12.968540649501888, 10.12645697634838, 8.570494387502801, 11.729018285236716, 7.830706490539565, 5.9856918575237295, 3.778605391111023, 5.3642140752245275, 4.488970669947518, 2.509033418493106, 1.3300879947343454, 0.0), # 14
(14.914403045230168, 14.943851236606186, 12.813442704156724, 13.754957036167184, 10.960905953224861, 5.403194588161918, 6.1136273613997005, 5.7127620903998375, 5.989917191325237, 2.917886228858997, 2.0686184900318456, 1.2041666274478897, 0.0, 15.00523748411101, 13.245832901926784, 10.343092450159226, 8.753658686576989, 11.979834382650473, 7.997866926559773, 6.1136273613997005, 3.8594247058299413, 5.480452976612431, 4.584985678722395, 2.562688540831345, 1.3585319306005625, 0.0), # 15
(15.204744536991681, 15.225012746328195, 13.054518490884568, 14.013797524530858, 11.170431372354487, 5.504869344073363, 6.228592311171181, 5.820055540734641, 6.102609728591085, 2.972751191784799, 2.1075527777238703, 1.2268191832913256, 0.0, 15.287650311237673, 13.495011016204579, 10.53776388861935, 8.918253575354395, 12.20521945718217, 8.148077757028497, 6.228592311171181, 3.932049531480973, 5.585215686177244, 4.671265841510287, 2.6109036981769136, 1.384092067848018, 0.0), # 16
(15.46229233554412, 15.472338288850588, 13.266581862056471, 14.241487410338536, 11.355466444708094, 5.594307507818667, 6.329722167784569, 5.914435736307213, 6.201739638212791, 3.021013725147788, 2.141801514207413, 1.2467455927818742, 0.0, 15.536075164610265, 13.714201520600614, 10.709007571037066, 9.063041175443361, 12.403479276425582, 8.280210030830098, 6.329722167784569, 3.9959339341561906, 5.677733222354047, 4.747162470112846, 2.6533163724112945, 1.4065762080773265, 0.0), # 17
(15.684973871120327, 15.683713681780135, 13.447820227079841, 14.436080628995136, 11.514473207155827, 5.670744771926737, 6.416152392186281, 5.995096283463507, 6.286459803987251, 3.0622612028174157, 2.171071955344136, 1.2637755403072954, 0.0, 15.748388926414954, 13.901530943380248, 10.855359776720679, 9.186783608452245, 12.572919607974502, 8.39313479684891, 6.416152392186281, 4.050531979947669, 5.757236603577914, 4.812026876331712, 2.689564045415968, 1.4257921528891033, 0.0), # 18
(15.870716573953118, 15.857024742723624, 13.596420995362104, 14.59563111590558, 11.645913696567856, 5.733416828926462, 6.4870184453227155, 6.061230788549498, 6.355923109711349, 3.0960809986631324, 2.1950713569957014, 1.2777387102553464, 0.0, 15.922468478837914, 14.055125812808807, 10.975356784978505, 9.288242995989394, 12.711846219422698, 8.485723103969297, 6.4870184453227155, 4.095297734947473, 5.822956848283928, 4.865210371968527, 2.7192841990724212, 1.441547703883966, 0.0), # 19
(16.01744787427533, 15.990157289287811, 13.710571576310672, 14.718192806474825, 11.748249949814339, 5.781559371346751, 6.54145578814029, 6.112032857911145, 6.409282439181973, 3.1220604865543846, 2.213506975023774, 1.2884647870137858, 0.0, 16.05619070406532, 14.17311265715164, 11.067534875118868, 9.366181459663151, 12.818564878363945, 8.556846001075604, 6.54145578814029, 4.129685265247679, 5.874124974907169, 4.9060642688249425, 2.7421143152621346, 1.4536506626625285, 0.0), # 20
(16.123095202319785, 16.080997139079486, 13.78845937933296, 14.801819636107783, 11.819944003765428, 5.8144080917165, 6.578599881585408, 6.1466960978944165, 6.445690676196012, 3.139787040360623, 2.226086065290016, 1.2957834549703726, 0.0, 16.147432484283325, 14.253618004674097, 11.13043032645008, 9.419361121081867, 12.891381352392024, 8.605374537052183, 6.578599881585408, 4.153148636940357, 5.909972001882714, 4.933939878702596, 2.757691875866592, 1.461908830825408, 0.0), # 21
(16.18558598831933, 16.12743010970541, 13.82827181383638, 14.844565540209405, 11.85945789529128, 5.83119868256461, 6.59758618660448, 6.164414114845277, 6.464300704550355, 3.148848033951298, 2.232515883656091, 1.2995243985128655, 0.0, 16.194070701678125, 14.294768383641518, 11.162579418280455, 9.446544101853892, 12.92860140910071, 8.630179760783388, 6.59758618660448, 4.1651419161175784, 5.92972894764564, 4.948188513403136, 2.7656543627672763, 1.4661300099732195, 0.0), # 22
(16.208629381348224, 16.132927937814358, 13.83323090992227, 14.849916975308645, 11.869580859768103, 5.833333333333334, 6.599843201807471, 6.166329218106997, 6.466627325102881, 3.149916909007774, 2.233322143243131, 1.2999863435451913, 0.0, 16.2, 14.299849778997103, 11.166610716215654, 9.44975072702332, 12.933254650205763, 8.632860905349796, 6.599843201807471, 4.166666666666667, 5.9347904298840515, 4.949972325102882, 2.7666461819844543, 1.4666298125285782, 0.0), # 23
(16.225619860854646, 16.12972098765432, 13.832419753086421, 14.849258333333335, 11.875314787855842, 5.833333333333334, 6.598603050108934, 6.163666666666667, 6.466315555555555, 3.149260246913581, 2.2332332210998884, 1.2998781893004117, 0.0, 16.2, 14.298660082304526, 11.166166105499443, 9.44778074074074, 12.93263111111111, 8.629133333333334, 6.598603050108934, 4.166666666666667, 5.937657393927921, 4.949752777777779, 2.7664839506172845, 1.4663382716049385, 0.0), # 24
(16.242251568338528, 16.1233996342021, 13.830818472793784, 14.847955246913582, 11.880922608634137, 5.833333333333334, 6.596159122085048, 6.158436213991771, 6.465699588477367, 3.1479675354366723, 2.233056906513697, 1.2996646852613931, 0.0, 16.2, 14.296311537875322, 11.165284532568485, 9.443902606310015, 12.931399176954734, 8.62181069958848, 6.596159122085048, 4.166666666666667, 5.940461304317068, 4.949318415637862, 2.766163694558757, 1.4657636031092822, 0.0), # 25
(16.258523230476854, 16.114060448102425, 13.828449016918157, 14.846022530864197, 11.886404126315846, 5.833333333333334, 6.592549374646977, 6.150736625514405, 6.46478732510288, 3.146060283493371, 2.2327947956935614, 1.2993487578113097, 0.0, 16.2, 14.292836335924404, 11.163973978467807, 9.43818085048011, 12.92957465020576, 8.611031275720167, 6.592549374646977, 4.166666666666667, 5.943202063157923, 4.948674176954733, 2.7656898033836312, 1.46491458619113, 0.0), # 26
(16.27443357394662, 16.1018, 13.825333333333333, 14.843475, 11.891759145113827, 5.833333333333334, 6.587811764705883, 6.140666666666667, 6.463586666666666, 3.143560000000001, 2.232448484848485, 1.2989333333333337, 0.0, 16.2, 14.288266666666669, 11.162242424242425, 9.430679999999999, 12.927173333333332, 8.596933333333334, 6.587811764705883, 4.166666666666667, 5.945879572556914, 4.947825000000001, 2.765066666666667, 1.4638000000000002, 0.0), # 27
(16.2899813254248, 16.08671486053955, 13.821493369913123, 14.840327469135804, 11.896987469240962, 5.833333333333334, 6.581984249172921, 6.12832510288066, 6.462105514403292, 3.140488193872886, 2.232019570187472, 1.2984213382106389, 0.0, 16.2, 14.282634720317025, 11.160097850937358, 9.421464581618656, 12.924211028806583, 8.579655144032923, 6.581984249172921, 4.166666666666667, 5.948493734620481, 4.946775823045269, 2.764298673982625, 1.462428623685414, 0.0), # 28
(16.3051652115884, 16.0689016003658, 13.816951074531323, 14.83659475308642, 11.902088902910101, 5.833333333333334, 6.575104784959253, 6.113810699588477, 6.460351769547325, 3.1368663740283504, 2.2315096479195247, 1.2978156988263985, 0.0, 16.2, 14.27597268709038, 11.157548239597624, 9.41059912208505, 12.92070353909465, 8.559334979423868, 6.575104784959253, 4.166666666666667, 5.951044451455051, 4.945531584362141, 2.763390214906265, 1.460809236396891, 0.0), # 29
(16.319983959114396, 16.04845679012346, 13.811728395061728, 14.832291666666666, 11.907063250334119, 5.833333333333334, 6.567211328976035, 6.097222222222222, 6.458333333333333, 3.1327160493827173, 2.230920314253648, 1.297119341563786, 0.0, 16.2, 14.268312757201645, 11.15460157126824, 9.398148148148149, 12.916666666666666, 8.536111111111111, 6.567211328976035, 4.166666666666667, 5.953531625167059, 4.944097222222223, 2.7623456790123457, 1.458950617283951, 0.0), # 30
(16.334436294679772, 16.02547700045725, 13.805847279378145, 14.82743302469136, 11.911910315725876, 5.833333333333334, 6.558341838134432, 6.078658436213992, 6.456058106995885, 3.1280587288523103, 2.2302531653988447, 1.296335192805975, 0.0, 16.2, 14.259687120865724, 11.151265826994223, 9.384176186556928, 12.91211621399177, 8.510121810699589, 6.558341838134432, 4.166666666666667, 5.955955157862938, 4.942477674897121, 2.761169455875629, 1.4568615454961138, 0.0), # 31
(16.34852094496153, 16.00005880201189, 13.799329675354366, 14.82203364197531, 11.916629903298237, 5.833333333333334, 6.548534269345599, 6.058218106995886, 6.453533991769548, 3.1229159213534534, 2.229509797564119, 1.2954661789361381, 0.0, 16.2, 14.250127968297518, 11.147548987820594, 9.368747764060357, 12.907067983539095, 8.48150534979424, 6.548534269345599, 4.166666666666667, 5.958314951649118, 4.940677880658438, 2.759865935070873, 1.4545508001828993, 0.0), # 32
(16.362236636636634, 15.972298765432097, 13.792197530864199, 14.816108333333332, 11.921221817264065, 5.833333333333334, 6.537826579520697, 6.0360000000000005, 6.450768888888889, 3.1173091358024703, 2.228691806958474, 1.2945152263374486, 0.0, 16.2, 14.239667489711932, 11.143459034792368, 9.351927407407409, 12.901537777777778, 8.450400000000002, 6.537826579520697, 4.166666666666667, 5.960610908632033, 4.938702777777778, 2.75843950617284, 1.452027160493827, 0.0), # 33
(16.375582096382097, 15.942293461362596, 13.784472793781436, 14.809671913580248, 11.92568586183623, 5.833333333333334, 6.526256725570888, 6.012102880658436, 6.447770699588479, 3.111259881115685, 2.2278007897909133, 1.2934852613930805, 0.0, 16.2, 14.228337875323884, 11.139003948954567, 9.333779643347052, 12.895541399176958, 8.41694403292181, 6.526256725570888, 4.166666666666667, 5.962842930918115, 4.93655730452675, 2.7568945587562874, 1.449299405578418, 0.0), # 34
(16.388556050874893, 15.9101394604481, 13.776177411979882, 14.802739197530864, 11.930021841227594, 5.833333333333334, 6.513862664407327, 5.986625514403293, 6.4445473251028815, 3.1047896662094203, 2.226838342270441, 1.2923792104862066, 0.0, 16.2, 14.216171315348271, 11.134191711352205, 9.314368998628257, 12.889094650205763, 8.381275720164611, 6.513862664407327, 4.166666666666667, 5.965010920613797, 4.934246399176955, 2.755235482395977, 1.4463763145861912, 0.0), # 35
(16.40115722679201, 15.87593333333333, 13.767333333333335, 14.795325, 11.934229559651024, 5.833333333333334, 6.500682352941176, 5.959666666666668, 6.441106666666666, 3.097920000000001, 2.225806060606061, 1.2912000000000003, 0.0, 16.2, 14.203200000000002, 11.129030303030303, 9.29376, 12.882213333333333, 8.343533333333335, 6.500682352941176, 4.166666666666667, 5.967114779825512, 4.931775000000001, 2.753466666666667, 1.4432666666666667, 0.0), # 36
(16.41338435081044, 15.839771650663007, 13.757962505715593, 14.78744413580247, 11.938308821319383, 5.833333333333334, 6.486753748083595, 5.931325102880659, 6.437456625514404, 3.090672391403751, 2.2247055410067764, 1.2899505563176348, 0.0, 16.2, 14.18945611949398, 11.123527705033881, 9.27201717421125, 12.874913251028808, 8.303855144032923, 6.486753748083595, 4.166666666666667, 5.969154410659692, 4.929148045267491, 2.751592501143119, 1.4399792409693644, 0.0), # 37
(16.425236149607162, 15.801750983081849, 13.748086877000459, 14.77911141975309, 11.942259430445535, 5.833333333333334, 6.4721148067457435, 5.901699588477367, 6.433605102880659, 3.0830683493369926, 2.22353837968159, 1.2886338058222835, 0.0, 16.2, 14.174971864045116, 11.11769189840795, 9.249205048010975, 12.867210205761317, 8.262379423868314, 6.4721148067457435, 4.166666666666667, 5.971129715222768, 4.926370473251031, 2.7496173754000917, 1.4365228166438047, 0.0), # 38
(16.436711349859177, 15.761967901234568, 13.737728395061731, 14.770341666666667, 11.94608119124235, 5.833333333333334, 6.456803485838781, 5.8708888888888895, 6.42956, 3.0751293827160504, 2.2223061728395064, 1.2872526748971194, 0.0, 16.2, 14.159779423868311, 11.111530864197531, 9.225388148148149, 12.85912, 8.219244444444445, 6.456803485838781, 4.166666666666667, 5.973040595621175, 4.923447222222223, 2.7475456790123465, 1.4329061728395065, 0.0), # 39
(16.44780867824346, 15.720518975765888, 13.726909007773205, 14.761149691358025, 11.949773907922687, 5.833333333333334, 6.440857742273865, 5.838991769547327, 6.425329218106996, 3.0668770004572488, 2.2210105166895295, 1.2858100899253166, 0.0, 16.2, 14.143910989178481, 11.105052583447646, 9.200631001371743, 12.850658436213992, 8.174588477366258, 6.440857742273865, 4.166666666666667, 5.974886953961343, 4.920383230452676, 2.745381801554641, 1.42913808870599, 0.0), # 40
(16.458526861437004, 15.677500777320528, 13.71565066300869, 14.751550308641978, 11.953337384699417, 5.833333333333334, 6.424315532962156, 5.806106995884774, 6.420920658436214, 3.05833271147691, 2.2196530074406624, 1.2843089772900476, 0.0, 16.2, 14.12739875019052, 11.09826503720331, 9.174998134430727, 12.841841316872427, 8.128549794238685, 6.424315532962156, 4.166666666666667, 5.976668692349708, 4.9171834362139935, 2.743130132601738, 1.4252273433927756, 0.0), # 41
(16.4688646261168, 15.633009876543213, 13.70397530864198, 14.741558333333336, 11.956771425785394, 5.833333333333334, 6.4072148148148145, 5.772333333333334, 6.416342222222223, 3.049518024691359, 2.2182352413019086, 1.282752263374486, 0.0, 16.2, 14.110274897119341, 11.091176206509541, 9.148554074074074, 12.832684444444446, 8.081266666666668, 6.4072148148148145, 4.166666666666667, 5.978385712892697, 4.913852777777779, 2.740795061728396, 1.421182716049383, 0.0), # 42
(16.47882069895983, 15.587142844078647, 13.69190489254687, 14.731188580246915, 11.960075835393496, 5.833333333333334, 6.389593544743001, 5.737769547325104, 6.4116018106995885, 3.040454449016919, 2.2167588144822714, 1.281142874561805, 0.0, 16.2, 14.092571620179852, 11.083794072411356, 9.121363347050755, 12.823203621399177, 8.032877366255146, 6.389593544743001, 4.166666666666667, 5.980037917696748, 4.9103961934156395, 2.738380978509374, 1.4170129858253318, 0.0), # 43
(16.488393806643085, 15.539996250571559, 13.679461362597166, 14.720455864197532, 11.963250417736582, 5.833333333333334, 6.371489679657872, 5.702514403292183, 6.4067073251028805, 3.031163493369914, 2.2152253231907557, 1.279483737235178, 0.0, 16.2, 14.074321109586954, 11.076126615953777, 9.09349048010974, 12.813414650205761, 7.983520164609057, 6.371489679657872, 4.166666666666667, 5.981625208868291, 4.906818621399179, 2.7358922725194335, 1.4127269318701419, 0.0), # 44
(16.497582675843546, 15.491666666666667, 13.66666666666667, 14.709375000000001, 11.966294977027516, 5.833333333333334, 6.352941176470589, 5.666666666666668, 6.4016666666666655, 3.021666666666668, 2.213636363636364, 1.277777777777778, 0.0, 16.2, 14.055555555555554, 11.068181818181818, 9.065000000000001, 12.803333333333331, 7.9333333333333345, 6.352941176470589, 4.166666666666667, 5.983147488513758, 4.903125000000001, 2.733333333333334, 1.4083333333333337, 0.0), # 45
(16.50638603323821, 15.442250663008686, 13.653542752629173, 14.697960802469137, 11.969209317479164, 5.833333333333334, 6.333985992092311, 5.63032510288066, 6.396487736625514, 3.0119854778235036, 2.2119935320281, 1.2760279225727789, 0.0, 16.2, 14.036307148300564, 11.059967660140499, 9.035956433470508, 12.792975473251028, 7.882455144032924, 6.333985992092311, 4.166666666666667, 5.984604658739582, 4.899320267489713, 2.730708550525835, 1.4038409693644263, 0.0), # 46
(16.514802605504055, 15.391844810242342, 13.640111568358483, 14.686228086419753, 11.971993243304391, 5.833333333333334, 6.3146620834341975, 5.593588477366255, 6.391178436213992, 3.0021414357567453, 2.210298424574968, 1.2742370980033535, 0.0, 16.2, 14.016608078036885, 11.051492122874839, 9.006424307270233, 12.782356872427984, 7.831023868312758, 6.3146620834341975, 4.166666666666667, 5.985996621652196, 4.895409362139919, 2.728022313671697, 1.3992586191129404, 0.0), # 47
(16.522831119318074, 15.340545679012347, 13.626395061728397, 14.674191666666669, 11.974646558716064, 5.833333333333334, 6.295007407407407, 5.556555555555557, 6.385746666666667, 2.9921560493827166, 2.208552637485971, 1.272408230452675, 0.0, 16.2, 13.996490534979422, 11.042763187429854, 8.976468148148149, 12.771493333333334, 7.77917777777778, 6.295007407407407, 4.166666666666667, 5.987323279358032, 4.891397222222224, 2.7252790123456796, 1.3945950617283953, 0.0), # 48
(16.53047030135726, 15.288449839963418, 13.612415180612713, 14.661866358024692, 11.977169067927047, 5.833333333333334, 6.275059920923102, 5.519325102880659, 6.380200329218106, 2.982050827617742, 2.2067577669701133, 1.2705442463039174, 0.0, 16.2, 13.97598670934309, 11.033788834850565, 8.946152482853226, 12.760400658436213, 7.727055144032923, 6.275059920923102, 4.166666666666667, 5.9885845339635235, 4.887288786008232, 2.7224830361225427, 1.389859076360311, 0.0), # 49
(16.537718878298588, 15.235653863740286, 13.598193872885233, 14.649266975308642, 11.979560575150202, 5.833333333333334, 6.25485758089244, 5.481995884773663, 6.3745473251028795, 2.971847279378144, 2.204915409236397, 1.2686480719402533, 0.0, 16.2, 13.955128791342785, 11.024577046181985, 8.91554183813443, 12.749094650205759, 7.674794238683129, 6.25485758089244, 4.166666666666667, 5.989780287575101, 4.883088991769548, 2.7196387745770467, 1.385059442158208, 0.0), # 50
(16.544575576819057, 15.182254320987655, 13.583753086419755, 14.636408333333335, 11.981820884598399, 5.833333333333334, 6.23443834422658, 5.4446666666666665, 6.368795555555556, 2.9615669135802474, 2.2030271604938276, 1.2667226337448563, 0.0, 16.2, 13.933948971193416, 11.015135802469137, 8.88470074074074, 12.737591111111112, 7.622533333333334, 6.23443834422658, 4.166666666666667, 5.9909104422991994, 4.878802777777779, 2.716750617283951, 1.380204938271605, 0.0), # 51
(16.551039123595647, 15.128347782350252, 13.56911476909008, 14.623305246913581, 11.983949800484496, 5.833333333333334, 6.213840167836683, 5.407436213991769, 6.3629529218107, 2.9512312391403754, 2.2010946169514076, 1.2647708581008996, 0.0, 16.2, 13.912479439109894, 11.005473084757037, 8.853693717421125, 12.7259058436214, 7.570410699588477, 6.213840167836683, 4.166666666666667, 5.991974900242248, 4.874435082304528, 2.713822953818016, 1.3753043438500232, 0.0), # 52
(16.55710824530535, 15.074030818472796, 13.554300868770008, 14.609972530864198, 11.985947127021364, 5.833333333333334, 6.1931010086339064, 5.370403292181071, 6.357027325102881, 2.940861764974852, 2.1991193748181406, 1.2627956713915565, 0.0, 16.2, 13.890752385307119, 10.995596874090701, 8.822585294924554, 12.714054650205762, 7.518564609053499, 6.1931010086339064, 4.166666666666667, 5.992973563510682, 4.8699908436214, 2.710860173754002, 1.3703664380429816, 0.0), # 53
(16.562781668625146, 15.019400000000001, 13.539333333333333, 14.596425, 11.987812668421869, 5.833333333333334, 6.172258823529412, 5.333666666666667, 6.351026666666667, 2.9304800000000006, 2.19710303030303, 1.2608000000000001, 0.0, 16.2, 13.8688, 10.98551515151515, 8.791440000000001, 12.702053333333334, 7.467133333333333, 6.172258823529412, 4.166666666666667, 5.993906334210934, 4.865475000000001, 2.707866666666667, 1.3654000000000004, 0.0), # 54
(16.568058120232035, 14.964551897576587, 13.524234110653865, 14.582677469135803, 11.989546228898869, 5.833333333333334, 6.151351569434358, 5.2973251028806585, 6.344958847736625, 2.9201074531321454, 2.1950471796150812, 1.2587867703094042, 0.0, 16.2, 13.846654473403445, 10.975235898075404, 8.760322359396435, 12.68991769547325, 7.416255144032922, 6.151351569434358, 4.166666666666667, 5.994773114449434, 4.860892489711935, 2.704846822130773, 1.360413808870599, 0.0), # 55
(16.572936326802996, 14.909583081847279, 13.509025148605396, 14.56874475308642, 11.991147612665237, 5.833333333333334, 6.130417203259905, 5.261477366255145, 6.338831769547324, 2.9097656332876096, 2.1929534189632958, 1.2567589087029418, 0.0, 16.2, 13.824347995732358, 10.964767094816478, 8.729296899862828, 12.677663539094649, 7.366068312757203, 6.130417203259905, 4.166666666666667, 5.995573806332619, 4.856248251028807, 2.7018050297210796, 1.3554166438042983, 0.0), # 56
(16.577415015015013, 14.85459012345679, 13.493728395061732, 14.554641666666669, 11.99261662393383, 5.833333333333334, 6.109493681917211, 5.226222222222224, 6.332653333333334, 2.899476049382717, 2.1908233445566783, 1.254719341563786, 0.0, 16.2, 13.801912757201645, 10.95411672278339, 8.69842814814815, 12.665306666666668, 7.316711111111113, 6.109493681917211, 4.166666666666667, 5.996308311966915, 4.851547222222224, 2.6987456790123465, 1.3504172839506174, 0.0), # 57
(16.581492911545087, 14.79966959304984, 13.478365797896664, 14.540383024691359, 11.99395306691752, 5.833333333333334, 6.088618962317438, 5.191658436213992, 6.326431440329218, 2.8892602103337914, 2.1886585526042324, 1.2526709952751107, 0.0, 16.2, 13.779380948026215, 10.943292763021162, 8.667780631001373, 12.652862880658436, 7.2683218106995895, 6.088618962317438, 4.166666666666667, 5.99697653345876, 4.846794341563787, 2.695673159579333, 1.3454245084590766, 0.0), # 58
(16.585168743070195, 14.744918061271147, 13.462959304983997, 14.525983641975309, 11.995156745829167, 5.833333333333334, 6.067831001371743, 5.157884773662552, 6.320173991769548, 2.879139625057157, 2.1864606393149604, 1.2506167962200887, 0.0, 16.2, 13.756784758420972, 10.9323031965748, 8.63741887517147, 12.640347983539096, 7.221038683127573, 6.067831001371743, 4.166666666666667, 5.9975783729145835, 4.841994547325104, 2.6925918609968, 1.3404470964791952, 0.0), # 59
(16.588441236267325, 14.690432098765434, 13.44753086419753, 14.511458333333334, 11.996227464881638, 5.833333333333334, 6.0471677559912855, 5.125000000000001, 6.31388888888889, 2.8691358024691365, 2.184231200897868, 1.2485596707818931, 0.0, 16.2, 13.734156378600822, 10.921156004489339, 8.607407407407408, 12.62777777777778, 7.175000000000001, 6.0471677559912855, 4.166666666666667, 5.998113732440819, 4.837152777777779, 2.6895061728395064, 1.3354938271604941, 0.0), # 60
(16.591309117813463, 14.636308276177413, 13.432102423411067, 14.496821913580249, 11.997165028287798, 5.833333333333334, 6.026667183087227, 5.093102880658437, 6.3075840329218105, 2.8592702514860546, 2.1819718335619576, 1.246502545343698, 0.0, 16.2, 13.711527998780674, 10.909859167809786, 8.577810754458163, 12.615168065843621, 7.130344032921811, 6.026667183087227, 4.166666666666667, 5.998582514143899, 4.832273971193417, 2.6864204846822135, 1.3305734796524924, 0.0), # 61
(16.593771114385607, 14.582643164151806, 13.416695930498403, 14.482089197530867, 11.997969240260517, 5.833333333333334, 6.006367239570725, 5.062292181069959, 6.301267325102881, 2.849564481024235, 2.1796841335162327, 1.2444483462886757, 0.0, 16.2, 13.68893180917543, 10.898420667581162, 8.548693443072704, 12.602534650205762, 7.0872090534979435, 6.006367239570725, 4.166666666666667, 5.998984620130258, 4.827363065843623, 2.6833391860996807, 1.3256948331047098, 0.0), # 62
(16.595825952660736, 14.529533333333333, 13.401333333333335, 14.467275000000003, 11.998639905012647, 5.833333333333334, 5.986305882352941, 5.0326666666666675, 6.294946666666666, 2.8400400000000006, 2.1773696969696976, 1.2424000000000002, 0.0, 16.2, 13.6664, 10.886848484848487, 8.52012, 12.589893333333332, 7.045733333333335, 5.986305882352941, 4.166666666666667, 5.999319952506323, 4.822425000000002, 2.6802666666666672, 1.3208666666666669, 0.0), # 63
(16.597472359315837, 14.477075354366713, 13.386036579789668, 14.452394135802471, 11.999176826757065, 5.833333333333334, 5.966521068345034, 5.004325102880659, 6.288629958847737, 2.830718317329676, 2.1750301201313547, 1.2403604328608446, 0.0, 16.2, 13.64396476146929, 10.875150600656774, 8.492154951989026, 12.577259917695473, 7.006055144032923, 5.966521068345034, 4.166666666666667, 5.999588413378532, 4.817464711934158, 2.6772073159579337, 1.316097759487883, 0.0), # 64
(16.5987090610279, 14.425365797896662, 13.370827617741199, 14.437461419753088, 11.999579809706631, 5.833333333333334, 5.947050754458163, 4.977366255144033, 6.282325102880659, 2.8216209419295843, 2.1726669992102097, 1.238332571254382, 0.0, 16.2, 13.6216582837982, 10.863334996051048, 8.464862825788751, 12.564650205761318, 6.968312757201646, 5.947050754458163, 4.166666666666667, 5.999789904853316, 4.812487139917697, 2.67416552354824, 1.3113968907178786, 0.0), # 65
(16.599534784473914, 14.374501234567903, 13.35572839506173, 14.422491666666668, 11.99984865807421, 5.833333333333334, 5.927932897603486, 4.95188888888889, 6.27604, 2.81276938271605, 2.170281930415264, 1.2363193415637863, 0.0, 16.2, 13.599512757201648, 10.851409652076319, 8.438308148148149, 12.55208, 6.932644444444446, 5.927932897603486, 4.166666666666667, 5.999924329037105, 4.807497222222223, 2.6711456790123465, 1.3067728395061733, 0.0), # 66
(16.59994825633087, 14.324578235025148, 13.340760859625059, 14.407499691358025, 11.999983176072671, 5.833333333333334, 5.909205454692165, 4.927991769547327, 6.269782551440329, 2.8041851486053964, 2.1678765099555233, 1.23432367017223, 0.0, 16.2, 13.577560371894528, 10.839382549777614, 8.412555445816189, 12.539565102880658, 6.899188477366257, 5.909205454692165, 4.166666666666667, 5.999991588036336, 4.802499897119342, 2.6681521719250116, 1.3022343850022864, 0.0), # 67
(16.59966658316932, 14.275431337669806, 13.325874599908552, 14.39237008856683, 11.999869818983834, 5.833225077478026, 5.890812155863717, 4.905562566681908, 6.263513519280598, 2.795848176658867, 2.1654095969441007, 1.2323373362532992, 0.0, 16.19980024005487, 13.555710698786289, 10.827047984720503, 8.3875445299766, 12.527027038561195, 6.867787593354672, 5.890812155863717, 4.166589341055733, 5.999934909491917, 4.797456696188944, 2.6651749199817103, 1.29776648524271, 0.0), # 68
(16.597026731078905, 14.22556009557945, 13.310651234567901, 14.376340217391304, 11.998838053740013, 5.832369272976682, 5.872214545077291, 4.8833991769547325, 6.256958847736625, 2.7875225562817723, 2.162630090377459, 1.2302958631145768, 0.0, 16.198217592592595, 13.533254494260342, 10.813150451887294, 8.362567668845315, 12.51391769547325, 6.8367588477366255, 5.872214545077291, 4.165978052126201, 5.999419026870006, 4.792113405797102, 2.66213024691358, 1.2932327359617684, 0.0), # 69
(16.59181726009423, 14.174735607770254, 13.295024577046181, 14.359304549114333, 11.996799268404205, 5.8306838388457045, 5.853328107649096, 4.861301630848957, 6.2500815424477985, 2.7791678097850943, 2.159506369740288, 1.228189701505708, 0.0, 16.195091735253776, 13.510086716562785, 10.797531848701441, 8.337503429355282, 12.500163084895597, 6.80582228318854, 5.853328107649096, 4.164774170604074, 5.998399634202102, 4.786434849704778, 2.6590049154092363, 1.2886123279791142, 0.0), # 70
(16.584111457028687, 14.122988247267578, 13.279000114311843, 14.341288204508857, 11.993779284004411, 5.828196087994717, 5.8341613276311906, 4.8392772443225125, 6.242891845755221, 2.7707841437370564, 2.1560499655423633, 1.226020391628362, 0.0, 16.190463820301783, 13.486224307911982, 10.780249827711817, 8.312352431211167, 12.485783691510441, 6.774988142051518, 5.8341613276311906, 4.162997205710512, 5.9968896420022055, 4.780429401502953, 2.6558000228623686, 1.2839080224788708, 0.0), # 71
(16.573982608695655, 14.070348387096773, 13.262583333333334, 14.322316304347826, 11.989803921568626, 5.824933333333335, 5.81472268907563, 4.817333333333334, 6.2354, 2.762371764705883, 2.1522724082934617, 1.2237894736842108, 0.0, 16.184375, 13.461684210526316, 10.761362041467306, 8.287115294117648, 12.4708, 6.744266666666667, 5.81472268907563, 4.160666666666668, 5.994901960784313, 4.7741054347826095, 2.6525166666666666, 1.2791225806451614, 0.0), # 72
(16.561504001908514, 14.016846400283198, 13.245779721079103, 14.302413969404189, 11.984899002124855, 5.820922887771173, 5.795020676034474, 4.795477213839354, 6.227616247523244, 2.753930879259798, 2.1481852285033574, 1.2214984878749227, 0.0, 16.1768664266118, 13.436483366624147, 10.740926142516786, 8.261792637779392, 12.455232495046488, 6.713668099375096, 5.795020676034474, 4.157802062693695, 5.992449501062428, 4.76747132313473, 2.649155944215821, 1.274258763662109, 0.0), # 73
(16.546748923480646, 13.962512659852205, 13.228594764517604, 14.281606320450884, 11.979090346701094, 5.816192064217854, 5.775063772559778, 4.773716201798507, 6.219550830666057, 2.7454616939670253, 2.143799956681829, 1.219148974402169, 0.0, 16.167979252400553, 13.410638718423858, 10.718999783409142, 8.236385081901075, 12.439101661332113, 6.683202682517909, 5.775063772559778, 4.154422903012753, 5.989545173350547, 4.760535440150296, 2.645718952903521, 1.269319332713837, 0.0), # 74
(16.52979066022544, 13.90737753882915, 13.211033950617283, 14.259918478260868, 11.972403776325345, 5.810768175582992, 5.754860462703601, 4.752057613168724, 6.211213991769547, 2.7369644153957884, 2.13912812333865, 1.2167424734676198, 0.0, 16.157754629629633, 13.384167208143815, 10.695640616693249, 8.210893246187364, 12.422427983539094, 6.652880658436215, 5.754860462703601, 4.150548696844995, 5.986201888162673, 4.7533061594202906, 2.6422067901234567, 1.2643070489844683, 0.0), # 75
(16.510702498956285, 13.851471410239393, 13.193102766346595, 14.237375563607085, 11.964865112025606, 5.804678534776205, 5.734419230517997, 4.730508763907942, 6.2026159731748205, 2.728439250114312, 2.134181258983598, 1.2142805252729445, 0.0, 16.146233710562413, 13.357085778002387, 10.67090629491799, 8.185317750342936, 12.405231946349641, 6.622712269471118, 5.734419230517997, 4.146198953411575, 5.982432556012803, 4.745791854535696, 2.638620553269319, 1.259224673658127, 0.0), # 76
(16.48955772648655, 13.794824647108282, 13.174806698673981, 14.21400269726248, 11.956500174829877, 5.797950454707109, 5.7137485600550235, 4.70907696997409, 6.193767017222985, 2.7198864046908207, 2.1289708941264505, 1.2117646700198144, 0.0, 16.13345764746228, 13.329411370217956, 10.64485447063225, 8.15965921407246, 12.38753403444597, 6.592707757963726, 5.7137485600550235, 4.141393181933649, 5.9782500874149385, 4.738000899087494, 2.6349613397347964, 1.254074967918935, 0.0), # 77
(16.46642962962963, 13.737467622461173, 13.156151234567902, 14.189825, 11.94733478576616, 5.790611248285322, 5.69285693536674, 4.687769547325104, 6.184677366255142, 2.711306085693537, 2.123508559276981, 1.2091964479098987, 0.0, 16.119467592592596, 13.301160927008882, 10.617542796384903, 8.13391825708061, 12.369354732510285, 6.562877366255145, 5.69285693536674, 4.136150891632373, 5.97366739288308, 4.729941666666668, 2.6312302469135807, 1.248860692951016, 0.0), # 78
(16.441391495198904, 13.679430709323423, 13.1371418609968, 14.164867592592593, 11.93739476586245, 5.782688228420464, 5.671752840505201, 4.666593811918916, 6.1753572626124065, 2.702698499690686, 2.117805784944966, 1.2065773991448674, 0.0, 16.104304698216733, 13.27235139059354, 10.58902892472483, 8.108095499072057, 12.350714525224813, 6.533231336686482, 5.671752840505201, 4.130491591728903, 5.968697382931225, 4.721622530864199, 2.6274283721993603, 1.243584609938493, 0.0), # 79
(16.414516610007755, 13.620744280720386, 13.117784064929126, 14.139155595813204, 11.92670593614675, 5.774208708022151, 5.650444759522465, 4.645557079713459, 6.165816948635879, 2.694063853250491, 2.111874101640184, 1.2039090639263914, 0.0, 16.08801011659808, 13.242999703190304, 10.559370508200919, 8.082191559751472, 12.331633897271757, 6.503779911598843, 5.650444759522465, 4.1244347914443935, 5.963352968073375, 4.713051865271069, 2.6235568129858255, 1.23824948006549, 0.0), # 80
(16.385878260869568, 13.56143870967742, 13.098083333333335, 14.112714130434785, 11.915294117647058, 5.765200000000001, 5.628941176470589, 4.624666666666667, 6.156066666666666, 2.685402352941177, 2.1057250398724086, 1.2011929824561405, 0.0, 16.070625, 13.213122807017545, 10.528625199362043, 8.05620705882353, 12.312133333333332, 6.474533333333334, 5.628941176470589, 4.118, 5.957647058823529, 4.704238043478263, 2.619616666666667, 1.2328580645161293, 0.0), # 81
(16.355549734597723, 13.501544369219879, 13.078045153177872, 14.085568317230274, 11.903185131391377, 5.75568941726363, 5.607250575401629, 4.603929888736474, 6.146116659045877, 2.676714205330967, 2.099370130151417, 1.198430694935785, 0.0, 16.052190500685874, 13.182737644293633, 10.496850650757084, 8.030142615992899, 12.292233318091753, 6.445501844231063, 5.607250575401629, 4.111206726616879, 5.951592565695688, 4.695189439076759, 2.6156090306355746, 1.2274131244745345, 0.0), # 82
(16.323604318005607, 13.441091632373114, 13.057675011431185, 14.057743276972625, 11.890404798407703, 5.745704272722655, 5.585381440367643, 4.5833540618808115, 6.135977168114616, 2.667999616988085, 2.0928209029869853, 1.195623741566995, 0.0, 16.03274777091907, 13.151861157236944, 10.464104514934926, 8.003998850964255, 12.271954336229232, 6.416695686633136, 5.585381440367643, 4.104074480516182, 5.945202399203851, 4.6859144256575425, 2.6115350022862374, 1.2219174211248287, 0.0), # 83
(16.290115297906603, 13.380110872162485, 13.036978395061729, 14.029264130434784, 11.876978939724037, 5.735271879286694, 5.563342255420687, 4.562946502057613, 6.125658436213991, 2.659258794480756, 2.0860888888888893, 1.1927736625514405, 0.0, 16.012337962962963, 13.120510288065844, 10.430444444444445, 7.977776383442267, 12.251316872427982, 6.388125102880658, 5.563342255420687, 4.096622770919067, 5.938489469862018, 4.676421376811596, 2.607395679012346, 1.2163737156511352, 0.0), # 84
(16.255155961114095, 13.318632461613346, 13.015960791037951, 14.000155998389694, 11.862933376368382, 5.724419549865368, 5.54114150461282, 4.542714525224815, 6.115170705685108, 2.650491944377203, 2.0791856183669055, 1.1898819980907918, 0.0, 15.991002229080934, 13.088701978998708, 10.395928091834525, 7.951475833131607, 12.230341411370215, 6.35980033531474, 5.54114150461282, 4.088871107046691, 5.931466688184191, 4.666718666129899, 2.6031921582075905, 1.210784769237577, 0.0), # 85
(16.21879959444146, 13.256686773751051, 12.994627686328306, 13.970444001610309, 11.84829392936873, 5.713174597368289, 5.518787671996097, 4.522665447340345, 6.104524218869075, 2.64169927324565, 2.0721226219308098, 1.1869502883867193, 0.0, 15.968781721536352, 13.05645317225391, 10.360613109654047, 7.9250978197369495, 12.20904843773815, 6.331731626276483, 5.518787671996097, 4.080838998120206, 5.924146964684365, 4.656814667203437, 2.5989255372656612, 1.2051533430682777, 0.0), # 86
(16.18111948470209, 13.194304181600955, 12.972984567901234, 13.940153260869565, 11.833086419753089, 5.7015643347050755, 5.496289241622575, 4.5028065843621405, 6.093729218106997, 2.6328809876543215, 2.0649114300903775, 1.1839800736408925, 0.0, 15.945717592592594, 13.023780810049816, 10.324557150451888, 7.898642962962963, 12.187458436213994, 6.303929218106997, 5.496289241622575, 4.072545953360768, 5.9165432098765445, 4.646717753623189, 2.594596913580247, 1.1994821983273598, 0.0), # 87
(16.142188918709373, 13.131515058188414, 12.951036922725194, 13.90930889694042, 11.817336668549451, 5.689616074785349, 5.473654697544313, 4.483145252248133, 6.082795945739979, 2.624037294171441, 2.0575635733553868, 1.1809728940549822, 0.0, 15.921850994513035, 12.990701834604803, 10.287817866776932, 7.8721118825143215, 12.165591891479957, 6.276403353147386, 5.473654697544313, 4.064011481989534, 5.908668334274726, 4.636436298980141, 2.5902073845450393, 1.193774096198947, 0.0), # 88
(16.102081183276677, 13.068349776538785, 12.928790237768634, 13.877936030595814, 11.80107049678582, 5.677357130518723, 5.4508925238133665, 4.463688766956257, 6.07173464410913, 2.6151683993652335, 2.050090582235612, 1.1779302898306583, 0.0, 15.897223079561043, 12.957233188137238, 10.250452911178058, 7.845505198095699, 12.14346928821826, 6.24916427373876, 5.4508925238133665, 4.055255093227659, 5.90053524839291, 4.625978676865272, 2.585758047553727, 1.1880317978671624, 0.0), # 89
(16.06086956521739, 13.004838709677419, 12.906250000000002, 13.846059782608698, 11.784313725490197, 5.664814814814815, 5.428011204481793, 4.444444444444445, 6.060555555555556, 2.606274509803922, 2.04250398724083, 1.1748538011695908, 0.0, 15.871875000000001, 12.923391812865496, 10.212519936204147, 7.818823529411765, 12.121111111111112, 6.222222222222222, 5.428011204481793, 4.046296296296297, 5.892156862745098, 4.615353260869567, 2.5812500000000003, 1.1822580645161291, 0.0), # 90
(16.0186273513449, 12.941012230629672, 12.883421696387746, 13.813705273752014, 11.767092175690575, 5.652016440583244, 5.405019223601649, 4.4254196006706294, 6.049268922420364, 2.597355832055731, 2.0348153188808165, 1.17174496827345, 0.0, 15.845847908093276, 12.889194651007948, 10.174076594404081, 7.792067496167191, 12.098537844840727, 6.195587440938882, 5.405019223601649, 4.037154600416603, 5.883546087845287, 4.604568424584006, 2.5766843392775494, 1.1764556573299705, 0.0), # 91
(15.975427828472597, 12.876900712420905, 12.86031081390032, 13.780897624798712, 11.749431668414964, 5.638989320733629, 5.381925065224994, 4.406621551592746, 6.037884987044658, 2.5884125726888843, 2.027036107665348, 1.1686053313439067, 0.0, 15.819182956104251, 12.85465864478297, 10.135180538326738, 7.765237718066651, 12.075769974089315, 6.169270172229845, 5.381925065224994, 4.027849514809735, 5.874715834207482, 4.593632541599572, 2.5720621627800644, 1.1706273374928098, 0.0), # 92
(15.931344283413848, 12.812534528076466, 12.836922839506174, 13.747661956521743, 11.731358024691357, 5.625760768175583, 5.358737213403881, 4.388057613168725, 6.026413991769548, 2.5794449382716054, 2.0191778841042, 1.1654364305826295, 0.0, 15.791921296296294, 12.819800736408922, 10.095889420521, 7.738334814814815, 12.052827983539096, 6.143280658436215, 5.358737213403881, 4.018400548696845, 5.865679012345678, 4.582553985507248, 2.567384567901235, 1.1647758661887697, 0.0), # 93
(15.886450002982048, 12.74794405062171, 12.813263260173755, 13.714023389694043, 11.712897065547754, 5.612358095818728, 5.335464152190369, 4.369735101356501, 6.014866178936138, 2.5704531353721194, 2.01125217870715, 1.16223980619129, 0.0, 15.764104080932785, 12.784637868104188, 10.056260893535747, 7.711359406116356, 12.029732357872277, 6.117629141899102, 5.335464152190369, 4.008827211299091, 5.856448532773877, 4.571341129898015, 2.5626526520347515, 1.1589040046019738, 0.0), # 94
(15.840818273990577, 12.683159653081995, 12.789337562871514, 13.680007045088567, 11.694074612012159, 5.598808616572678, 5.312114365636515, 4.351661332114007, 6.003251790885536, 2.561437370558649, 2.0032705219839726, 1.1590169983715575, 0.0, 15.735772462277092, 12.749186982087132, 10.016352609919863, 7.684312111675945, 12.006503581771073, 6.09232586495961, 5.312114365636515, 3.999149011837627, 5.847037306006079, 4.560002348362857, 2.5578675125743033, 1.1530145139165453, 0.0), # 95
(15.79452238325282, 12.61821170848268, 12.765151234567902, 13.645638043478261, 11.674916485112563, 5.585139643347051, 5.288696337794377, 4.333843621399177, 5.991581069958848, 2.55239785039942, 1.9952444444444448, 1.1557695473251033, 0.0, 15.706967592592594, 12.713465020576134, 9.976222222222225, 7.657193551198258, 11.983162139917695, 6.067381069958849, 5.288696337794377, 3.9893854595336076, 5.8374582425562815, 4.5485460144927545, 2.553030246913581, 1.1471101553166074, 0.0), # 96
(15.747635617582157, 12.553130589849111, 12.740709762231369, 13.61094150563607, 11.655448505876976, 5.571378489051465, 5.265218552716011, 4.316289285169945, 5.979864258497181, 2.5433347814626543, 1.9871854765983423, 1.152498993253596, 0.0, 15.677730624142663, 12.677488925789556, 9.93592738299171, 7.630004344387961, 11.959728516994362, 6.042804999237923, 5.265218552716011, 3.9795560636081895, 5.827724252938488, 4.536980501878691, 2.5481419524462736, 1.141193689986283, 0.0), # 97
(15.700231263791975, 12.487946670206647, 12.71601863283036, 13.575942552334945, 11.635696495333388, 5.557552466595541, 5.241689494453475, 4.299005639384241, 5.968111598841639, 2.5342483703165772, 1.9791051489554419, 1.1492068763587067, 0.0, 15.648102709190674, 12.64127563994577, 9.89552574477721, 7.60274511094973, 11.936223197683278, 6.018607895137937, 5.241689494453475, 3.969680333282529, 5.817848247666694, 4.525314184111649, 2.5432037265660723, 1.1352678791096953, 0.0), # 98
(15.652382608695653, 12.422690322580646, 12.691083333333335, 13.540666304347827, 11.615686274509805, 5.543688888888889, 5.218117647058825, 4.282000000000001, 5.956333333333333, 2.5251388235294123, 1.9710149920255189, 1.1458947368421055, 0.0, 15.618125000000001, 12.604842105263158, 9.855074960127594, 7.575416470588236, 11.912666666666667, 5.9948000000000015, 5.218117647058825, 3.9597777777777776, 5.807843137254903, 4.51355543478261, 2.5382166666666675, 1.129335483870968, 0.0), # 99
(15.60416293910658, 12.357391919996457, 12.665909350708734, 13.505137882447666, 11.595443664434223, 5.529815068841132, 5.194511494584116, 4.265279682975157, 5.944539704313367, 2.516006347669384, 1.9629265363183495, 1.1425641149054624, 0.0, 15.58783864883402, 12.568205263960085, 9.814632681591746, 7.54801904300815, 11.889079408626735, 5.97139155616522, 5.194511494584116, 3.9498679063150943, 5.797721832217111, 4.501712627482556, 2.533181870141747, 1.1233992654542237, 0.0), # 100
(15.555645541838135, 12.292081835479447, 12.640502171925013, 13.469382407407409, 11.574994486134646, 5.515958319361886, 5.17087952108141, 4.248852004267642, 5.932740954122847, 2.506851149304716, 1.9548513123437101, 1.1392165507504473, 0.0, 15.557284807956103, 12.531382058254918, 9.77425656171855, 7.520553447914146, 11.865481908245695, 5.948392805974699, 5.17087952108141, 3.9399702281156324, 5.787497243067323, 4.48979413580247, 2.528100434385003, 1.1174619850435863, 0.0), # 101
(15.506903703703706, 12.22679044205496, 12.614867283950618, 13.433425000000002, 11.554364560639069, 5.5021459533607695, 5.1472302106027605, 4.2327242798353915, 5.920947325102881, 2.497673435003632, 1.9468008506113774, 1.135853584578731, 0.0, 15.526504629629631, 12.49438943036604, 9.734004253056886, 7.493020305010894, 11.841894650205761, 5.925813991769548, 5.1472302106027605, 3.93010425240055, 5.7771822803195345, 4.477808333333335, 2.522973456790124, 1.1115264038231782, 0.0), # 102
(15.458010711516671, 12.161548112748353, 12.589010173754001, 13.397290780998391, 11.533579708975497, 5.488405283747397, 5.123572047200224, 4.2169038256363365, 5.909169059594573, 2.4884734113343563, 1.9387866816311266, 1.132476756591983, 0.0, 15.495539266117968, 12.457244322511812, 9.693933408155633, 7.4654202340030675, 11.818338119189146, 5.903665355890872, 5.123572047200224, 3.920289488390998, 5.766789854487748, 4.465763593666131, 2.5178020347508006, 1.1055952829771232, 0.0), # 103
(15.409039852090416, 12.096385220584981, 12.562936328303612, 13.361004871175524, 11.512665752171923, 5.474763623431389, 5.099913514925861, 4.201397957628411, 5.897416399939034, 2.479251284865113, 1.9308203359127338, 1.129087606991874, 0.0, 15.464429869684501, 12.419963676910612, 9.654101679563668, 7.437753854595337, 11.794832799878067, 5.881957140679775, 5.099913514925861, 3.9105454453081343, 5.756332876085962, 4.4536682903918425, 2.5125872656607227, 1.099671383689544, 0.0), # 104
(15.360064412238325, 12.031332138590201, 12.536651234567902, 13.324592391304346, 11.491648511256354, 5.461248285322361, 5.076263097831727, 4.186213991769549, 5.885699588477366, 2.470007262164126, 1.922913343965976, 1.125687675980074, 0.0, 15.433217592592593, 12.382564435780811, 9.61456671982988, 7.410021786492376, 11.771399176954732, 5.860699588477368, 5.076263097831727, 3.9008916323731144, 5.745824255628177, 4.44153079710145, 2.5073302469135803, 1.093757467144564, 0.0), # 105
(15.311157678773782, 11.96641923978937, 12.510160379515318, 13.28807846215781, 11.470553807256785, 5.44788658232993, 5.052629279969876, 4.1713592440176805, 5.8740288675506775, 2.4607415497996183, 1.9150772363006283, 1.1222785037582528, 0.0, 15.401943587105624, 12.345063541340778, 9.575386181503141, 7.382224649398854, 11.748057735101355, 5.839902941624753, 5.052629279969876, 3.8913475588070923, 5.735276903628392, 4.429359487385938, 2.5020320759030636, 1.0878562945263066, 0.0), # 106
(15.26239293851017, 11.901676897207842, 12.483469250114315, 13.251488204508856, 11.449407461201215, 5.434705827363715, 5.0290205453923695, 4.156841030330743, 5.862414479500076, 2.451454354339816, 1.9073235434264675, 1.1188616305280807, 0.0, 15.370649005486968, 12.307477935808887, 9.536617717132337, 7.354363063019447, 11.724828959000153, 5.819577442463041, 5.0290205453923695, 3.8819327338312255, 5.724703730600607, 4.417162734836286, 2.496693850022863, 1.081970627018895, 0.0), # 107
(15.21384347826087, 11.83713548387097, 12.456583333333336, 13.214846739130437, 11.428235294117645, 5.421733333333335, 5.0054453781512604, 4.142666666666667, 5.850866666666667, 2.442145882352942, 1.8996637958532698, 1.1154385964912283, 0.0, 15.339375000000002, 12.26982456140351, 9.498318979266347, 7.326437647058825, 11.701733333333333, 5.799733333333334, 5.0054453781512604, 3.8726666666666674, 5.714117647058822, 4.40494891304348, 2.4913166666666675, 1.076103225806452, 0.0), # 108
(15.16558258483927, 11.772825372804107, 12.429508116140834, 13.17817918679549, 11.40706312703408, 5.408996413148403, 4.98191226229861, 4.128843468983388, 5.839395671391555, 2.4328163404072196, 1.8921095240908108, 1.112010941849365, 0.0, 15.308162722908094, 12.232120360343014, 9.460547620454054, 7.298449021221657, 11.67879134278311, 5.780380856576743, 4.98191226229861, 3.8635688665345733, 5.70353156351704, 4.392726395598498, 2.485901623228167, 1.0702568520731008, 0.0), # 109
(15.117683545058746, 11.708776937032614, 12.402249085505263, 13.141510668276972, 11.385916780978512, 5.396522379718539, 4.9584296818864715, 4.1153787532388355, 5.828011736015851, 2.423465935070874, 1.8846722586488671, 1.108580206804162, 0.0, 15.277053326474624, 12.194382274845779, 9.423361293244335, 7.27039780521262, 11.656023472031702, 5.76153025453437, 4.9584296818864715, 3.8546588426560997, 5.692958390489256, 4.380503556092325, 2.4804498171010527, 1.0644342670029652, 0.0), # 110
(15.07021964573269, 11.64502054958184, 12.374811728395064, 13.104866304347826, 11.36482207697894, 5.384338545953361, 4.935006120966905, 4.102279835390947, 5.816725102880659, 2.4140948729121283, 1.8773635300372145, 1.1051479315572885, 0.0, 15.246087962962964, 12.156627247130173, 9.386817650186073, 7.242284618736384, 11.633450205761317, 5.743191769547326, 4.935006120966905, 3.845956104252401, 5.68241103848947, 4.368288768115943, 2.474962345679013, 1.0586382317801675, 0.0), # 111
(15.02326417367448, 11.581586583477144, 12.347201531778696, 13.068271215781, 11.34380483606337, 5.372472224762486, 4.911650063591967, 4.089554031397653, 5.805546014327083, 2.404703360499207, 1.8701948687656293, 1.101715656310415, 0.0, 15.215307784636488, 12.118872219414563, 9.350974343828147, 7.214110081497619, 11.611092028654166, 5.725375643956714, 4.911650063591967, 3.837480160544633, 5.671902418031685, 4.356090405260334, 2.469440306355739, 1.0528715075888313, 0.0), # 112
(14.976806757924871, 11.51861130755273, 12.319490437669426, 13.031800658990448, 11.322854058851952, 5.3609451179335466, 4.888420770925416, 4.077235045853738, 5.794513499337931, 2.3953218946450923, 1.8631797083074313, 1.098292391533924, 0.0, 15.184710241349155, 12.081216306873161, 9.315898541537155, 7.185965683935276, 11.589026998675863, 5.708129064195233, 4.888420770925416, 3.829246512809676, 5.661427029425976, 4.343933552996817, 2.4638980875338854, 1.0471464825047938, 0.0), # 113
(14.930369436640104, 11.456715869170786, 12.292060900028826, 12.995747305532802, 11.301752911537415, 5.349730967961242, 4.865614566728464, 4.065474173003413, 5.783796819046966, 2.3861260671651134, 1.8563318232301862, 1.094921622948397, 0.0, 15.154040662656056, 12.044137852432362, 9.28165911615093, 7.1583782014953385, 11.567593638093932, 5.691663842204779, 4.865614566728464, 3.821236405686601, 5.6508764557687075, 4.331915768510935, 2.4584121800057654, 1.0415196244700715, 0.0), # 114
(14.883815844806392, 11.395922558068468, 12.264929243609757, 12.960101406218136, 11.280434856414509, 5.338800611665514, 4.84324772015325, 4.054268436185806, 5.773399988623354, 2.3771301311952313, 1.8496412030472253, 1.091605011007847, 0.0, 15.123210610656603, 12.007655121086316, 9.248206015236125, 7.131390393585693, 11.546799977246708, 5.675975810660129, 4.84324772015325, 3.8134290083325095, 5.640217428207254, 4.320033802072713, 2.452985848721952, 1.0359929598244064, 0.0), # 115
(14.837087797180216, 11.336142812561162, 12.238042919978499, 12.924799380319683, 11.25886776147603, 5.328128285467958, 4.821283854022315, 4.043586875265996, 5.763296714254843, 2.3683173433798195, 1.8430949150057288, 1.0883364263316462, 0.0, 15.092171615609425, 11.971700689648106, 9.215474575028642, 7.104952030139457, 11.526593428509686, 5.661021625372395, 4.821283854022315, 3.8058059181913984, 5.629433880738015, 4.308266460106562, 2.4476085839957, 1.0305584375055605, 0.0), # 116
(14.790127108518035, 11.277288070964257, 12.211349380701316, 12.88977764711069, 11.237019494714783, 5.317688225790165, 4.799686591158202, 4.033398530109057, 5.753460702129175, 2.359670960363252, 1.8366800263528757, 1.085109739539167, 0.0, 15.06087520777316, 11.936207134930834, 9.183400131764378, 7.079012881089755, 11.50692140425835, 5.6467579421526795, 4.799686591158202, 3.7983487327072605, 5.6185097473573915, 4.296592549036898, 2.4422698761402635, 1.0252080064512963, 0.0), # 117
(14.742875593576338, 11.21926977159314, 12.18479607734449, 12.854972625864399, 11.214857924123566, 5.3074546690537305, 4.7784195543834524, 4.023672440580065, 5.743865658434098, 2.351174238789904, 1.8303836043358468, 1.0819188212497801, 0.0, 15.02927291740644, 11.901107033747579, 9.151918021679233, 7.053522716369711, 11.487731316868196, 5.633141416812091, 4.7784195543834524, 3.791039049324093, 5.607428962061783, 4.284990875288134, 2.436959215468898, 1.0199336155993766, 0.0), # 118
(14.695275067111588, 11.161999352763203, 12.158330461474298, 12.820320735854047, 11.192350917695169, 5.297401851680244, 4.757446366520605, 4.014377646544097, 5.734485289357356, 2.3428104353041492, 1.824192716201821, 1.0787575420828581, 0.0, 14.997316274767892, 11.866332962911438, 9.120963581009105, 7.028431305912447, 11.468970578714712, 5.620128705161736, 4.757446366520605, 3.7838584654858884, 5.5961754588475845, 4.273440245284683, 2.43166609229486, 1.014727213887564, 0.0), # 119
(14.647267343880259, 11.105388252789831, 12.131899984657018, 12.785758396352872, 11.169466343422396, 5.287504010091301, 4.736730650392203, 4.005483187866229, 5.7252933010866975, 2.3345628065503625, 1.818094429197978, 1.0756197726577732, 0.0, 14.964956810116156, 11.831817499235502, 9.090472145989889, 7.003688419651086, 11.450586602173395, 5.60767646301272, 4.736730650392203, 3.7767885786366437, 5.584733171711198, 4.2619194654509585, 2.4263799969314035, 1.0095807502536214, 0.0), # 120
(14.59879423863883, 11.049347909988416, 12.105452098458917, 12.751222026634121, 11.146172069298046, 5.277735380708496, 4.716236028820784, 3.9969581044115383, 5.716263399809866, 2.326414609172919, 1.812075810571498, 1.0724993835938965, 0.0, 14.932146053709857, 11.797493219532859, 9.060379052857488, 6.979243827518756, 11.432526799619732, 5.595741346176154, 4.716236028820784, 3.769810986220354, 5.573086034649023, 4.250407342211375, 2.4210904196917835, 1.0044861736353108, 0.0), # 121
(14.549797566143766, 10.993789762674343, 12.078934254446281, 12.716648045971027, 11.122435963314915, 5.268070199953418, 4.695926124628894, 3.9887714360450994, 5.707369291714607, 2.3183490998161913, 1.8061239275695606, 1.0693902455106004, 0.0, 14.898835535807633, 11.763292700616601, 9.030619637847803, 6.955047299448573, 11.414738583429214, 5.584280010463139, 4.695926124628894, 3.762907285681013, 5.561217981657458, 4.238882681990344, 2.4157868508892566, 0.9994354329703949, 0.0), # 122
(14.50021914115155, 10.938625249163001, 12.052293904185383, 12.681972873636834, 11.098225893465804, 5.258482704247664, 4.675764560639071, 3.9808922226319887, 5.698584682988669, 2.3103495351245553, 1.8002258474393456, 1.0662862290272563, 0.0, 14.864976786668116, 11.729148519299818, 9.001129237196727, 6.931048605373665, 11.397169365977337, 5.573249111684785, 4.675764560639071, 3.7560590744626166, 5.549112946732902, 4.227324291212279, 2.4104587808370765, 0.9944204771966367, 0.0), # 123
(14.450000778418648, 10.883765807769782, 12.025478499242494, 12.647132928904785, 11.073509727743506, 5.248947130012824, 4.655714959673856, 3.9732895040372846, 5.689883279819794, 2.302399171742385, 1.794368637428032, 1.063181204763237, 0.0, 14.830521336549939, 11.694993252395603, 8.971843187140161, 6.907197515227153, 11.379766559639588, 5.562605305652198, 4.655714959673856, 3.74924795000916, 5.536754863871753, 4.215710976301596, 2.405095699848499, 0.9894332552517985, 0.0), # 124
(14.399084292701534, 10.82912287681007, 11.9984354911839, 12.612064631048113, 11.048255334140823, 5.239437713670492, 4.635740944555791, 3.965932320126061, 5.68123878839573, 2.294481266314054, 1.7885393647828007, 1.0600690433379134, 0.0, 14.795420715711726, 11.660759476717045, 8.942696823914003, 6.883443798942161, 11.36247757679146, 5.552305248176485, 4.635740944555791, 3.7424555097646373, 5.524127667070411, 4.204021543682705, 2.39968709823678, 0.9844657160736429, 0.0), # 125
(14.347411498756685, 10.774607894599258, 11.971112331575865, 12.576704399340066, 11.022430580650552, 5.229928691642264, 4.615806138107416, 3.958789710763395, 5.6726249149042225, 2.2865790754839375, 1.7827250967508306, 1.0569436153706582, 0.0, 14.759626454412127, 11.626379769077237, 8.913625483754151, 6.859737226451811, 11.345249829808445, 5.542305595068753, 4.615806138107416, 3.735663351173045, 5.511215290325276, 4.192234799780023, 2.394222466315173, 0.9795098085999328, 0.0), # 126
(14.294924211340579, 10.720132299452729, 11.943456471984673, 12.54098865305388, 10.996003335265492, 5.220394300349728, 4.595874163151275, 3.951830715814364, 5.664015365533016, 2.27867585589641, 1.7769129005793014, 1.0537987914808424, 0.0, 14.723090082909758, 11.591786706289264, 8.884564502896506, 6.836027567689229, 11.328030731066033, 5.53256300214011, 4.595874163151275, 3.728853071678377, 5.498001667632746, 4.1803295510179606, 2.388691294396935, 0.97455748176843, 0.0), # 127
(14.241564245209673, 10.665607529685879, 11.915415363976601, 12.504853811462798, 10.968941465978443, 5.210808776214481, 4.575908642509906, 3.9450243751440417, 5.655383846469858, 2.2707548641958457, 1.7710898435153934, 1.0506284422878387, 0.0, 14.68576313146326, 11.556912865166222, 8.855449217576966, 6.812264592587535, 11.310767692939717, 5.523034125201659, 4.575908642509906, 3.722006268724629, 5.484470732989221, 4.168284603820934, 2.3830830727953205, 0.9696006845168982, 0.0), # 128
(14.187273415120451, 10.610945023614088, 11.886936459117921, 12.468236293840059, 10.9412128407822, 5.201146355658116, 4.555873199005851, 3.938339728617507, 5.646704063902494, 2.2627993570266187, 1.765242992806286, 1.0474264384110183, 0.0, 14.647597130331262, 11.5216908225212, 8.82621496403143, 6.788398071079855, 11.293408127804987, 5.51367562006451, 4.555873199005851, 3.7151045397557967, 5.4706064203911, 4.156078764613354, 2.377387291823584, 0.9646313657830989, 0.0), # 129
(14.131993535829388, 10.556056219552751, 11.857967208974907, 12.431072519458905, 10.91278532766956, 5.191381275102222, 4.53573145546165, 3.9317458160998338, 5.637949724018666, 2.2547925910331035, 1.7593594156991588, 1.044186650469754, 0.0, 14.608543609772397, 11.48605315516729, 8.796797078495793, 6.764377773099309, 11.275899448037332, 5.504444142539767, 4.53573145546165, 3.7081294822158726, 5.45639266383478, 4.1436908398196355, 2.3715934417949813, 0.9596414745047956, 0.0), # 130
(14.07566642209295, 10.500852555817252, 11.828455065113841, 12.393298907592571, 10.883626794633326, 5.181487770968396, 4.515447034699847, 3.9252116774560997, 5.629094533006126, 2.2467178228596745, 1.7534261794411918, 1.0409029490834167, 0.0, 14.568554100045299, 11.449932439917582, 8.767130897205957, 6.740153468579022, 11.258189066012251, 5.49529634843854, 4.515447034699847, 3.701062693548854, 5.441813397316663, 4.131099635864191, 2.3656910130227686, 0.9546229596197504, 0.0), # 131
(14.018233888667616, 10.445245470722984, 11.798347479100995, 12.354851877514303, 10.853705109666297, 5.171440079678229, 4.49498355954298, 3.918706352551382, 5.620112197052615, 2.238558309150706, 1.7474303512795641, 1.0375692048713792, 0.0, 14.527580131408602, 11.413261253585167, 8.73715175639782, 6.715674927452117, 11.24022439410523, 5.486188893571935, 4.49498355954298, 3.693885771198735, 5.4268525548331485, 4.1182839591714355, 2.3596694958201994, 0.949567770065726, 0.0), # 132
(13.959637750309861, 10.38914640258533, 11.767591902502646, 12.315667848497343, 10.822988140761264, 5.161212437653315, 4.474304652813592, 3.9121988812507547, 5.61097642234588, 2.2302973065505736, 1.7413589984614566, 1.0341792884530125, 0.0, 14.485573234120938, 11.375972172983136, 8.706794992307282, 6.690891919651719, 11.22195284469176, 5.477078433751057, 4.474304652813592, 3.686580312609511, 5.411494070380632, 4.105222616165782, 2.3535183805005295, 0.9444678547804848, 0.0), # 133
(13.899819821776152, 10.332466789719687, 11.736135786885072, 12.275683239814924, 10.791443755911033, 5.150779081315248, 4.453373937334223, 3.9056583034192958, 5.601660915073669, 2.2219180717036497, 1.7351991882340478, 1.030727070447689, 0.0, 14.442484938440934, 11.337997774924577, 8.675995941170239, 6.6657542151109475, 11.203321830147338, 5.467921624787015, 4.453373937334223, 3.6791279152251772, 5.395721877955516, 4.091894413271643, 2.3472271573770147, 0.9393151627017899, 0.0), # 134
(13.838721917822966, 10.275118070441435, 11.703926583814546, 12.234834470740296, 10.759039823108395, 5.14011424708562, 4.432155035927415, 3.8990536589220803, 5.592139381423722, 2.213403861254311, 1.7289379878445184, 1.0272064214747805, 0.0, 14.398266774627231, 11.299270636222584, 8.64468993922259, 6.640211583762932, 11.184278762847445, 5.458675122490913, 4.432155035927415, 3.671510176489728, 5.379519911554198, 4.0782781569134325, 2.340785316762909, 0.9341016427674034, 0.0), # 135
(13.776285853206776, 10.217011683065968, 11.670911744857346, 12.193057960546685, 10.725744210346152, 5.129192171386024, 4.410611571415708, 3.892353987624185, 5.5823855275837895, 2.2047379318469296, 1.7225624645400475, 1.0236112121536591, 0.0, 14.352870272938459, 11.259723333690248, 8.612812322700236, 6.614213795540787, 11.164771055167579, 5.44929558267386, 4.410611571415708, 3.6637086938471604, 5.362872105173076, 4.064352653515563, 2.3341823489714693, 0.9288192439150881, 0.0), # 136
(13.712453442684055, 10.15805906590867, 11.63703872157975, 12.15029012850735, 10.691524785617101, 5.117987090638052, 4.388707166621645, 3.885528329390686, 5.572373059741617, 2.1959035401258813, 1.716059685567815, 1.0199353131036961, 0.0, 14.306246963633242, 11.219288444140656, 8.580298427839075, 6.587710620377642, 11.144746119483234, 5.439739661146961, 4.388707166621645, 3.6557050647414657, 5.345762392808551, 4.050096709502451, 2.3274077443159498, 0.9234599150826065, 0.0), # 137
(13.647166501011277, 10.098171657284933, 11.602254965548024, 12.106467393895517, 10.656349416914047, 5.106473241263299, 4.366405444367763, 3.8785457240866603, 5.56207568408495, 2.1868839427355393, 1.7094167181750008, 1.016172594944264, 0.0, 14.258348376970226, 11.1778985443869, 8.547083590875005, 6.560651828206616, 11.1241513681699, 5.4299640137213245, 4.366405444367763, 3.6474808866166426, 5.3281747084570235, 4.035489131298506, 2.320450993109605, 0.9180156052077213, 0.0), # 138
(13.58036684294491, 10.037260895510144, 11.566507928328454, 12.061526175984431, 10.620185972229777, 5.094624859683358, 4.343670027476608, 3.8713752115771833, 5.551467106801532, 2.1776623963202795, 1.7026206296087845, 1.0123169282947344, 0.0, 14.20912604320803, 11.135486211242075, 8.513103148043921, 6.532987188960837, 11.102934213603064, 5.419925296208056, 4.343670027476608, 3.6390177569166844, 5.3100929861148884, 4.020508725328145, 2.313301585665691, 0.912478263228195, 0.0), # 139
(13.511996283241437, 9.97523821889969, 11.529745061487317, 12.015402894047334, 10.583002319557098, 5.082416182319821, 4.320464538770717, 3.863985831727331, 5.54052103407911, 2.168222157524475, 1.6956584871163454, 1.008362183774479, 0.0, 14.158531492605304, 11.091984021519266, 8.478292435581725, 6.504666472573423, 11.08104206815822, 5.409580164418264, 4.320464538770717, 3.6302972730855863, 5.291501159778549, 4.005134298015779, 2.3059490122974635, 0.9068398380817901, 0.0), # 140
(13.44199663665733, 9.912015065768964, 11.491913816590882, 11.968033967357464, 10.544766326888803, 5.069821445594281, 4.296752601072636, 3.8563466244021805, 5.529211172105429, 2.158546482992501, 1.688517357944864, 1.00430223200287, 0.0, 14.106516255420662, 11.047324552031569, 8.442586789724318, 6.4756394489775015, 11.058422344210857, 5.398885274163053, 4.296752601072636, 3.6213010325673434, 5.272383163444402, 3.989344655785822, 2.2983827633181764, 0.9010922787062696, 0.0), # 141
(13.37030971794905, 9.84750287443335, 11.452961645205429, 11.919355815188066, 10.505445862217693, 5.056814885928333, 4.272497837204901, 3.848426629466808, 5.517511227068235, 2.1486186293687317, 1.6811843093415195, 1.0001309435992793, 0.0, 14.053031861912746, 11.001440379592072, 8.405921546707596, 6.445855888106194, 11.03502245413647, 5.3877972812535315, 4.272497837204901, 3.612010632805952, 5.252722931108846, 3.973118605062689, 2.2905923290410857, 0.8952275340393956, 0.0), # 142
(13.29687734187308, 9.781613083208239, 11.412835998897235, 11.86930485681237, 10.465008793536564, 5.043370739743566, 4.247663869990055, 3.840194886786288, 5.505394905155279, 2.1384218532975416, 1.6736464085534917, 0.9958421891830788, 0.0, 13.998029842340188, 10.954264081013864, 8.368232042767458, 6.415265559892624, 11.010789810310557, 5.376272841500803, 4.247663869990055, 3.6024076712454045, 5.232504396768282, 3.956434952270791, 2.282567199779447, 0.8892375530189309, 0.0), # 143
(13.221641323185896, 9.714257130409019, 11.37148432923257, 11.817817511503629, 10.423422988838217, 5.029463243461577, 4.222214322250639, 3.8316204362256996, 5.492835912554298, 2.1279394114233043, 1.6658907228279605, 0.99142983937364, 0.0, 13.941461726961624, 10.905728233110038, 8.329453614139801, 6.383818234269912, 10.985671825108597, 5.364268610715979, 4.222214322250639, 3.592473745329698, 5.2117114944191085, 3.9392725038345437, 2.2742968658465146, 0.8831142845826383, 0.0), # 144
(13.144543476643964, 9.64534645435108, 11.328854087777719, 11.764830198535075, 10.380656316115449, 5.015066633503958, 4.196112816809195, 3.8226723176501176, 5.479807955453042, 2.1171545603903956, 1.6579043194121055, 0.9868877647903354, 0.0, 13.88327904603568, 10.855765412693687, 8.289521597060528, 6.351463681171186, 10.959615910906084, 5.351741244710165, 4.196112816809195, 3.582190452502827, 5.190328158057724, 3.921610066178359, 2.265770817555544, 0.8768496776682801, 0.0), # 145
(13.065525617003761, 9.574792493349808, 11.284892726098956, 11.710279337179951, 10.33667664336106, 5.000155146292303, 4.169322976488264, 3.813319570924618, 5.4662847400392565, 2.1060505568431886, 1.6496742655531065, 0.9822098360525362, 0.0, 13.82343332982099, 10.804308196577896, 8.248371327765533, 6.318151670529565, 10.932569480078513, 5.338647399294466, 4.169322976488264, 3.5715393902087875, 5.16833832168053, 3.903426445726651, 2.2569785452197917, 0.870435681213619, 0.0), # 146
(12.98452955902176, 9.502506685720592, 11.239547695762546, 11.654101346711496, 10.291451838567841, 4.984703018248201, 4.141808424110385, 3.803531235914277, 5.4522399725006885, 2.094610657426059, 1.6411876284981433, 0.9773899237796149, 0.0, 13.761876108576189, 10.751289161575762, 8.205938142490716, 6.2838319722781755, 10.904479945001377, 5.324943730279988, 4.141808424110385, 3.5605021558915717, 5.145725919283921, 3.884700448903833, 2.2479095391525097, 0.8638642441564175, 0.0), # 147
(12.901497117454435, 9.428400469778822, 11.192766448334778, 11.596232646402957, 10.2449497697286, 4.968684485793251, 4.113532782498101, 3.7932763524841717, 5.437647359025082, 2.082818118783379, 1.6324314754943956, 0.9724218985909429, 0.0, 13.698558912559907, 10.69664088450037, 8.162157377471978, 6.248454356350136, 10.875294718050164, 5.310586893477841, 4.113532782498101, 3.5490603469951787, 5.1224748848643, 3.8654108821343196, 2.2385532896669558, 0.8571273154344385, 0.0), # 148
(12.81637010705826, 9.352385283839885, 11.144496435381926, 11.536609655527563, 10.197138304836129, 4.9520737853490395, 4.084459674473953, 3.7825239604993777, 5.42248060580018, 2.0706561975595257, 1.6233928737890426, 0.9672996311058923, 0.0, 13.63343327203078, 10.640295942164814, 8.116964368945213, 6.211968592678575, 10.84496121160036, 5.295533544699129, 4.084459674473953, 3.5371955609635997, 5.098569152418064, 3.845536551842522, 2.2288992870763855, 0.8502168439854443, 0.0), # 149
(12.729090342589704, 9.274372566219169, 11.09468510847026, 11.475168793358566, 10.147985311883227, 4.934845153337166, 4.054552722860481, 3.771243099824971, 5.406713419013735, 2.058108150398871, 1.614058890629265, 0.9620169919438353, 0.0, 13.566450717247434, 10.582186911382186, 8.070294453146325, 6.174324451196611, 10.81342683802747, 5.27974033975496, 4.054552722860481, 3.524889395240833, 5.0739926559416135, 3.825056264452856, 2.2189370216940523, 0.8431247787471974, 0.0), # 150
(12.63959963880524, 9.194273755232066, 11.043279919166057, 11.411846479169196, 10.097458658862696, 4.916972826179219, 4.023775550480226, 3.759402810326029, 5.390319504853488, 2.0451572339457917, 1.6044165932622414, 0.956567851724143, 0.0, 13.49756277846851, 10.522246368965572, 8.022082966311206, 6.135471701837374, 10.780639009706976, 5.263163934456441, 4.023775550480226, 3.5121234472708704, 5.048729329431348, 3.8039488263897328, 2.2086559838332116, 0.8358430686574607, 0.0), # 151
(12.54783981046135, 9.11200028919396, 10.990228319035603, 11.346579132232703, 10.045526213767326, 4.898431040296793, 3.992091780155732, 3.7469721318676275, 5.373272569507184, 2.0317867048446603, 1.5944530489351527, 0.950946081066188, 0.0, 13.426720985952636, 10.460406891728066, 7.9722652446757625, 6.09536011453398, 10.746545139014367, 5.245760984614678, 3.992091780155732, 3.4988793144977093, 5.022763106883663, 3.7821930440775686, 2.198045663807121, 0.8283636626539964, 0.0), # 152
(12.453752672314497, 9.027463606420243, 10.935477759645158, 11.27930317182232, 9.992155844589925, 4.8791940321114815, 3.9594650347095355, 3.7339201043148416, 5.355546319162572, 2.017979819739852, 1.5841553248951779, 0.945145550589342, 0.0, 13.353876869958444, 10.39660105648276, 7.920776624475889, 6.053939459219555, 10.711092638325145, 5.227488146040779, 3.9594650347095355, 3.485138594365344, 4.996077922294963, 3.759767723940774, 2.187095551929032, 0.8206785096745677, 0.0), # 153
(12.357280039121166, 8.940575145226303, 10.878975692561012, 11.209955017211293, 9.937315419323285, 4.859236038044878, 3.9258589369641825, 3.7202157675327485, 5.337114460007395, 2.0037198352757417, 1.5735104883894968, 0.9391601309129768, 0.0, 13.278981960744572, 10.330761440042743, 7.867552441947483, 6.011159505827224, 10.67422892001479, 5.208302074545848, 3.9258589369641825, 3.4708828843177697, 4.968657709661643, 3.736651672403765, 2.1757951385122025, 0.8127795586569367, 0.0), # 154
(12.258363725637818, 8.851246343927524, 10.820669569349436, 11.138471087672855, 9.880972805960209, 4.838531294518574, 3.891237109742209, 3.705828161386424, 5.317950698229401, 1.9889900080967022, 1.562505606665289, 0.9329836926564644, 0.0, 13.201987788569642, 10.262820619221108, 7.812528033326444, 5.966970024290106, 10.635901396458802, 5.188159425940994, 3.891237109742209, 3.456093781798981, 4.940486402980104, 3.712823695890952, 2.1641339138698874, 0.804658758538866, 0.0), # 155
(12.15694554662093, 8.759388640839303, 10.760506841576703, 11.06478780248025, 9.823095872493491, 4.817054037954164, 3.85556317586616, 3.690726325740946, 5.298028740016334, 1.9737735948471096, 1.5511277469697347, 0.9266101064391765, 0.0, 13.122845883692296, 10.19271117083094, 7.755638734848673, 5.921320784541328, 10.596057480032668, 5.167016856037325, 3.85556317586616, 3.440752884252974, 4.911547936246746, 3.688262600826751, 2.1521013683153405, 0.7963080582581185, 0.0), # 156
(12.05296731682698, 8.664913474277022, 10.698434960809092, 10.988841580906726, 9.76365248691593, 4.79477850477324, 3.8188007581585754, 3.6748793004613884, 5.27732229155594, 1.958053852171337, 1.5393639765500133, 0.9200332428804852, 0.0, 13.041507776371162, 10.120365671685335, 7.696819882750066, 5.87416155651401, 10.55464458311188, 5.1448310206459436, 3.8188007581585754, 3.4248417891237426, 4.881826243457965, 3.662947193635576, 2.1396869921618182, 0.7877194067524566, 0.0), # 157
(11.943489514248384, 8.56599791046598, 10.631455938536474, 10.907723497981493, 9.699926512929064, 4.7702895112293024, 3.780085376742286, 3.6571979682329148, 5.254219782186185, 1.9413463665164579, 1.5268255340103847, 0.9130132752259121, 0.0, 12.954377375064553, 10.043146027485031, 7.634127670051924, 5.824039099549372, 10.50843956437237, 5.120077155526081, 3.780085376742286, 3.407349650878073, 4.849963256464532, 3.6359078326604983, 2.126291187707295, 0.7787270827696345, 0.0), # 158
(11.811658827165445, 8.452495802079234, 10.542317091203984, 10.804772590546145, 9.61620406376707, 4.7354436714732975, 3.734570210708573, 3.6314756885095885, 5.21942787265181, 1.9209123976394986, 1.5113111828317318, 0.9041816698244146, 0.0, 12.840684235072311, 9.94599836806856, 7.556555914158659, 5.762737192918495, 10.43885574530362, 5.084065963913424, 3.734570210708573, 3.3824597653380692, 4.808102031883535, 3.6015908635153826, 2.108463418240797, 0.7684087092799304, 0.0), # 159
(11.655795351846896, 8.323475201859713, 10.429227943941186, 10.678293012490633, 9.51084814010325, 4.689385209644506, 3.6817949987070273, 3.5970661263515646, 5.171960121188613, 1.896482260745158, 1.4926025356292107, 0.893400259851713, 0.0, 12.69827297422973, 9.827402858368842, 7.463012678146054, 5.689446782235472, 10.343920242377227, 5.0358925768921905, 3.6817949987070273, 3.3495608640317895, 4.755424070051625, 3.559431004163545, 2.0858455887882372, 0.7566795638054286, 0.0), # 160
(11.477155287337537, 8.179777273184687, 10.293395962547079, 10.529487004508074, 9.38495266590092, 4.632672092132293, 3.622145156805501, 3.5544003554065204, 5.112442542399476, 1.8682632772683756, 1.4708644412265888, 0.8807689958543429, 0.0, 12.528598471710556, 9.68845895439777, 7.354322206132943, 5.6047898318051255, 10.224885084798952, 4.976160497569129, 3.622145156805501, 3.3090514943802094, 4.69247633295046, 3.509829001502692, 2.058679192509416, 0.7436161157440625, 0.0), # 161
(11.27699483268217, 8.022243179431417, 10.136028612820661, 10.359556807291593, 9.239611565123418, 4.565862285326026, 3.5560061010718473, 3.503909449322135, 5.041501150887273, 1.836462768644093, 1.4462617484476323, 0.8663878283788393, 0.0, 12.333115606688533, 9.530266112167231, 7.231308742238162, 5.509388305932278, 10.083002301774545, 4.9054732290509895, 3.5560061010718473, 3.261330203804304, 4.619805782561709, 3.4531856024305316, 2.0272057225641325, 0.7292948344937653, 0.0), # 162
(11.056570186925597, 7.851714083977169, 9.958333360560937, 10.169704661534322, 9.075918761734068, 4.489513755615068, 3.4837632475739206, 3.4460244817460834, 4.959761961254883, 1.8012880563072504, 1.418959306116109, 0.8503567079717379, 0.0, 12.113279258337407, 9.353923787689116, 7.0947965305805445, 5.40386416892175, 9.919523922509766, 4.824434274444517, 3.4837632475739206, 3.2067955397250487, 4.537959380867034, 3.3899015538447745, 1.9916666721121876, 0.71379218945247, 0.0), # 163
(10.817137549112616, 7.669031150199204, 9.761517671566903, 9.961132807929381, 8.894968179696201, 4.404184469388787, 3.405802012379573, 3.3811765263260463, 4.867850988105186, 1.762946461692788, 1.3891219630557858, 0.8327755851795738, 0.0, 11.870544305830926, 9.160531436975312, 6.945609815278928, 5.288839385078362, 9.735701976210372, 4.733647136856465, 3.405802012379573, 3.1458460495634197, 4.447484089848101, 3.320377602643128, 1.9523035343133808, 0.6971846500181095, 0.0), # 164
(10.559953118288028, 7.475035541474793, 9.546789011637559, 9.735043487169904, 8.697853742973145, 4.310432393036548, 3.3225078115566578, 3.3097966567096977, 4.766394246041056, 1.7216453062356458, 1.35691456809043, 0.8137444105488828, 0.0, 11.606365628342832, 8.951188516037709, 6.7845728404521495, 5.164935918706936, 9.532788492082112, 4.633715319393577, 3.3225078115566578, 3.078880280740391, 4.348926871486572, 3.245014495723302, 1.909357802327512, 0.6795486855886177, 0.0), # 165
(10.286273093496636, 7.270568421181199, 9.315354846571905, 9.492638939949002, 8.485669375528229, 4.208815492947715, 3.234266061173029, 3.2323159465447184, 4.656017749665372, 1.6775919113707654, 1.322501970043808, 0.7933631346262003, 0.0, 11.322198105046873, 8.726994480888202, 6.612509850219039, 5.0327757341122945, 9.312035499330744, 4.525242325162606, 3.234266061173029, 3.0062967806769394, 4.242834687764114, 3.1642129799830014, 1.8630709693143812, 0.6609607655619273, 0.0), # 166
(9.997353673783238, 7.056470952695688, 9.06842264216894, 9.235121406959813, 8.259509001324778, 4.099891735511655, 3.14146217729654, 3.1491654694787847, 4.537347513581013, 1.6309935985330861, 1.2860490177396875, 0.7717317079580612, 0.0, 11.019496615116793, 8.489048787538673, 6.430245088698436, 4.892980795599257, 9.074695027162026, 4.408831657270299, 3.14146217729654, 2.928494096794039, 4.129754500662389, 3.0783738023199385, 1.8136845284337881, 0.6414973593359717, 0.0), # 167
(9.694451058192634, 6.833584299395522, 8.807199864227664, 8.963693128895455, 8.020466544326124, 3.9842190871177325, 3.0444815759950434, 3.0607762991595733, 4.411009552390856, 1.5820576891575493, 1.247720560001835, 0.7489500810910016, 0.0, 10.69971603772634, 8.238450892001017, 6.2386028000091756, 4.746173067472647, 8.822019104781711, 4.285086818823403, 3.0444815759950434, 2.8458707765126663, 4.010233272163062, 2.987897709631819, 1.7614399728455332, 0.6212349363086839, 0.0), # 168
(9.378821445769624, 6.602749624657969, 8.53289397854708, 8.67955634644906, 7.769635928495594, 3.8623555141553156, 2.9437096733363934, 2.9675795092347634, 4.277629880697781, 1.5309915046790952, 1.2076814456540184, 0.7251182045715564, 0.0, 10.364311252049257, 7.976300250287119, 6.038407228270092, 4.592974514037284, 8.555259761395561, 4.154611312928669, 2.9437096733363934, 2.7588253672537966, 3.884817964247797, 2.8931854488163538, 1.706578795709416, 0.6002499658779973, 0.0), # 169
(9.051721035559014, 6.3648080918602945, 8.24671245092618, 8.383913300313743, 7.508111077796515, 3.7348589830137664, 2.8395318853884426, 2.870006173352032, 4.137834513104661, 1.4780023665326634, 1.1660965235200045, 0.7003360289462612, 0.0, 10.014737137259289, 7.7036963184088725, 5.830482617600023, 4.43400709959799, 8.275669026209322, 4.018008642692845, 2.8395318853884426, 2.6677564164384044, 3.7540555388982577, 2.7946377667712485, 1.649342490185236, 0.5786189174418451, 0.0), # 170
(8.7144060266056, 6.12060086437976, 7.949862747163971, 8.077966231182643, 7.23698591619222, 3.602287460082452, 2.7323336282190445, 2.7684873651590554, 3.992249464214377, 1.4232975961531957, 1.1231306424235596, 0.6747035047616515, 0.0, 9.652448572530185, 7.421738552378166, 5.615653212117798, 4.269892788459586, 7.984498928428754, 3.8758823112226777, 2.7323336282190445, 2.5730624714874657, 3.61849295809611, 2.692655410394215, 1.5899725494327943, 0.5564182603981601, 0.0), # 171
(8.368132617954185, 5.870969105593635, 7.643552333059449, 7.762917379748876, 6.9573543676460305, 3.4651989117507385, 2.6225003178960526, 2.663454158303514, 3.8415007486298056, 1.3670845149756323, 1.0789486511884518, 0.648320582564263, 0.0, 9.278900437035686, 7.1315264082068905, 5.3947432559422595, 4.101253544926896, 7.683001497259611, 3.7288358216249198, 2.6225003178960526, 2.475142079821956, 3.4786771838230153, 2.587639126582959, 1.52871046661189, 0.5337244641448761, 0.0), # 172
(8.014157008649567, 5.616753978879182, 7.328988674411616, 7.439968986705571, 6.6703103561212815, 3.3241513044079904, 2.51041737048732, 2.5553376264330825, 3.6862143809538255, 1.309570444434913, 1.0337153986384477, 0.62128721290063, 0.0, 8.89554760994954, 6.83415934190693, 5.168576993192238, 3.9287113333047383, 7.372428761907651, 3.5774726770063157, 2.51041737048732, 2.37439378886285, 3.3351551780606408, 2.479989662235191, 1.4657977348823235, 0.5106139980799257, 0.0), # 173
(7.6537353977365505, 5.358796647613667, 7.00737923701947, 7.110323292745849, 6.376947805581297, 3.179702604443573, 2.3964702020607005, 2.4445688431954404, 3.527016375789314, 1.250962705965979, 0.9875957335973142, 0.5937033463172892, 0.0, 8.503844970445494, 6.53073680949018, 4.93797866798657, 3.7528881178979363, 7.054032751578628, 3.4223963804736166, 2.3964702020607005, 2.2712161460311235, 3.1884739027906486, 2.370107764248617, 1.401475847403894, 0.4871633316012425, 0.0), # 174
(7.288123984259929, 5.097938275174352, 6.679931486682011, 6.7751825385628415, 6.078360639989406, 3.0324107782468537, 2.2810442286840464, 2.331578882238264, 3.36453274773915, 1.19146862100377, 0.9407545048888186, 0.5656689333607753, 0.0, 8.105247397697292, 6.222358266968527, 4.703772524444093, 3.574405863011309, 6.7290654954783, 3.26421043513357, 2.2810442286840464, 2.1660076987477526, 3.039180319994703, 2.2583941795209475, 1.3359862973364023, 0.46344893410675936, 0.0), # 175
(6.91857896726451, 4.835020024938507, 6.347852889198238, 6.435748964849671, 5.775642783308939, 2.882833792207196, 2.164524866425212, 2.216798817209233, 3.199389511406209, 1.131295510983227, 0.8933565613367281, 0.537283924577624, 0.0, 7.701209770878679, 5.910123170353863, 4.46678280668364, 3.39388653294968, 6.398779022812418, 3.103518344092926, 2.164524866425212, 2.0591669944337117, 2.8878213916544695, 2.1452496549498905, 1.2695705778396478, 0.4395472749944098, 0.0), # 176
(6.546356545795092, 4.570883060283395, 6.012350910367152, 6.093224812299459, 5.469888159503225, 2.731529612713966, 2.0472975313520503, 2.100659721756022, 3.0322126813933705, 1.07065069733929, 0.8455667517648098, 0.5086482705143706, 0.0, 7.2931869691634, 5.595130975658075, 4.227833758824048, 3.211952092017869, 6.064425362786741, 2.9409236104584306, 2.0472975313520503, 1.9510925805099755, 2.7349440797516125, 2.0310749374331536, 1.2024701820734305, 0.4155348236621269, 0.0), # 177
(6.172712918896475, 4.306368544586282, 5.6746330159877525, 5.74881232160534, 5.162190692535588, 2.5790562061565305, 1.929747639532414, 1.9835926695263104, 2.863628272303512, 1.0097415015069002, 0.7975499249968301, 0.4798619217175504, 0.0, 6.882633871725203, 5.278481138893053, 3.98774962498415, 3.0292245045207, 5.727256544607024, 2.7770297373368344, 1.929747639532414, 1.8421830043975218, 2.581095346267794, 1.916270773868447, 1.1349266031975505, 0.3914880495078438, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
21, # 1
)
|
n = int(input())
teams = [int(x) for x in input().split()]
carrying = 0
for i in range(n):
if teams[i] == 0 and carrying == 1:
print("NO")
exit()
if teams[i] % 2 == 1:
if carrying == 0:
carrying = 1
else:
carrying = 0
if carrying == 0:
print("YES")
else:
print("NO")
|
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2013 Midokura PTE LTD.
# All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
APPLICATION_OCTET_STREAM = "application/octet-stream"
APPLICATION_JSON_V5 = "application/vnd.org.midonet.Application-v5+json"
APPLICATION_ERROR_JSON = "application/vnd.org.midonet.Error-v1+json"
APPLICATION_TENANT_JSON = "application/vnd.org.midonet.Tenant-v1+json"
APPLICATION_TENANT_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Tenant-v1+json"
APPLICATION_ROUTER_JSON = "application/vnd.org.midonet.Router-v3+json"
APPLICATION_ROUTER_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Router-v3+json"
APPLICATION_BRIDGE_JSON = "application/vnd.org.midonet.Bridge-v3+json"
APPLICATION_BRIDGE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Bridge-v3+json"
APPLICATION_HOST_JSON = "application/vnd.org.midonet.Host-v2+json"
APPLICATION_HOST_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Host-v2+json"
APPLICATION_INTERFACE_JSON = "application/vnd.org.midonet.Interface-v1+json"
APPLICATION_INTERFACE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Interface-v1+json"
APPLICATION_HOST_COMMAND_JSON = \
"application/vnd.org.midonet.HostCommand-v1+json"
APPLICATION_HOST_COMMAND_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.HostCommand-v1+json"
APPLICATION_PORT_LINK_JSON = "application/vnd.org.midonet.PortLink-v1+json"
APPLICATION_ROUTE_JSON = "application/vnd.org.midonet.Route-v1+json"
APPLICATION_ROUTE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Route-v1+json"
APPLICATION_PORTGROUP_JSON = "application/vnd.org.midonet.PortGroup-v1+json"
APPLICATION_PORTGROUP_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.PortGroup-v1+json"
APPLICATION_PORTGROUP_PORT_JSON = \
"application/vnd.org.midonet.PortGroupPort-v1+json"
APPLICATION_PORTGROUP_PORT_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.PortGroupPort-v1+json"
APPLICATION_CHAIN_JSON = "application/vnd.org.midonet.Chain-v1+json"
APPLICATION_CHAIN_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Chain-v1+json"
APPLICATION_RULE_JSON = "application/vnd.org.midonet.Rule-v2+json"
APPLICATION_RULE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Rule-v2+json"
APPLICATION_BGP_JSON = "application/vnd.org.midonet.Bgp-v1+json"
APPLICATION_BGP_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Bgp-v1+json"
APPLICATION_AD_ROUTE_JSON = "application/vnd.org.midonet.AdRoute-v1+json"
APPLICATION_AD_ROUTE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.AdRoute-v1+json"
APPLICATION_BGP_NETWORK_JSON = "application/vnd.org.midonet.BgpNetwork-v1+json"
APPLICATION_BGP_NETWORK_COLLECTION_JSON =\
"application/vnd.org.midonet.collection.BgpNetwork-v1+json"
APPLICATION_BGP_PEER_JSON = "application/vnd.org.midonet.BgpPeer-v1+json"
APPLICATION_BGP_PEER_COLLECTION_JSON =\
"application/vnd.org.midonet.collection.BgpPeer-v1+json"
APPLICATION_VPN_JSON = "application/vnd.org.midonet.Vpn-v1+json"
APPLICATION_VPN_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Vpn-v1+json"
APPLICATION_DHCP_SUBNET_JSON = "application/vnd.org.midonet.DhcpSubnet-v2+json"
APPLICATION_DHCP_SUBNET_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.DhcpSubnet-v2+json"
APPLICATION_DHCP_HOST_JSON = "application/vnd.org.midonet.DhcpHost-v1+json"
APPLICATION_DHCP_HOST_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.DhcpHost-v1+json"
APPLICATION_DHCPV6_SUBNET_JSON = \
"application/vnd.org.midonet.DhcpV6Subnet-v1+json"
APPLICATION_DHCPV6_SUBNET_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.DhcpV6Subnet-v1+json"
APPLICATION_DHCPV6_HOST_JSON = "application/vnd.org.midonet.DhcpV6Host-v1+json"
APPLICATION_DHCPV6_HOST_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.DhcpV6Host-v1+json"
APPLICATION_MONITORING_QUERY_RESPONSE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.mgmt.MetricQueryResponse-v1+json"
APPLICATION_MONITORING_QUERY_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.MetricQuery-v1+json"
APPLICATION_METRICS_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Metric-v1+json"
APPLICATION_METRIC_TARGET_JSON = \
"application/vnd.org.midonet.MetricTarget-v1+json"
APPLICATION_TUNNEL_ZONE_JSON = "application/vnd.org.midonet.TunnelZone-v1+json"
APPLICATION_TUNNEL_ZONE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.TunnelZone-v1+json"
APPLICATION_TUNNEL_ZONE_HOST_JSON = \
"application/vnd.org.midonet.TunnelZoneHost-v1+json"
APPLICATION_TUNNEL_ZONE_HOST_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.TunnelZoneHost-v1+json"
APPLICATION_GRE_TUNNEL_ZONE_HOST_JSON = \
"application/vnd.org.midonet.GreTunnelZoneHost-v1+json"
APPLICATION_GRE_TUNNEL_ZONE_HOST_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.GreTunnelZoneHost-v1+json"
APPLICATION_HOST_INTERFACE_PORT_JSON = \
"application/vnd.org.midonet.HostInterfacePort-v1+json"
APPLICATION_HOST_INTERFACE_PORT_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.HostInterfacePort-v1+json"
APPLICATION_CONDITION_JSON = "application/vnd.org.midonet.Condition-v1+json"
APPLICATION_CONDITION_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Condition-v1+json"
APPLICATION_TRACE_JSON = "application/vnd.org.midonet.Trace-v1+json"
APPLICATION_TRACE_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Trace-v1+json"
APPLICATION_WRITE_VERSION_JSON = \
"application/vnd.org.midonet.WriteVersion-v1+json"
APPLICATION_SYSTEM_STATE_JSON = \
"application/vnd.org.midonet.SystemState-v2+json"
APPLICATION_HOST_VERSION_JSON = \
"application/vnd.org.midonet.HostVersion-v1+json"
# Port media types
APPLICATION_PORT_JSON = "application/vnd.org.midonet.Port-v2+json"
APPLICATION_PORT_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Port-v2+json"
APPLICATION_IP_ADDR_GROUP_JSON = \
"application/vnd.org.midonet.IpAddrGroup-v1+json"
APPLICATION_IP_ADDR_GROUP_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.IpAddrGroup-v1+json"
APPLICATION_IP_ADDR_GROUP_ADDR_JSON = \
"application/vnd.org.midonet.IpAddrGroupAddr-v1+json"
APPLICATION_IP_ADDR_GROUP_ADDR_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.IpAddrGroupAddr-v1+json"
# L4LB media types
APPLICATION_LOAD_BALANCER_JSON = \
"application/vnd.org.midonet.LoadBalancer-v1+json"
APPLICATION_LOAD_BALANCER_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.LoadBalancer-v1+json"
APPLICATION_VIP_JSON = "application/vnd.org.midonet.VIP-v1+json"
APPLICATION_VIP_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.VIP-v1+json"
APPLICATION_POOL_JSON = "application/vnd.org.midonet.Pool-v1+json"
APPLICATION_POOL_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.Pool-v1+json"
APPLICATION_POOL_MEMBER_JSON = "application/vnd.org.midonet.PoolMember-v1+json"
APPLICATION_POOL_MEMBER_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.PoolMember-v1+json"
APPLICATION_HEALTH_MONITOR_JSON = \
"application/vnd.org.midonet.HealthMonitor-v1+json"
APPLICATION_HEALTH_MONITOR_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.HealthMonitor-v1+json"
APPLICATION_POOL_STATISTIC_JSON = \
"application/vnd.org.midonet.PoolStatistic-v1+json"
APPLICATION_POOL_STATISTIC_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.PoolStatistic-v1+json"
# VxGW
APPLICATION_VTEP_JSON = "application/vnd.org.midonet.VTEP-v1+json"
APPLICATION_VTEP_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.VTEP-v1+json"
APPLICATION_VTEP_BINDING_JSON = \
"application/vnd.org.midonet.VTEPBinding-v1+json"
APPLICATION_VTEP_BINDING_COLLECTION_JSON = \
"application/vnd.org.midonet.collection.VTEPBinding-v1+json"
|
def compute_epsg(lon, lat):
"""
Compute the EPSG code of the UTM zone which contains
the point with given longitude and latitude
Args:
lon (float): longitude of the point
lat (float): latitude of the point
Returns:
int: EPSG code
"""
# UTM zone number starts from 1 at longitude -180,
# and increments by 1 every 6 degrees of longitude
zone = int((lon + 180) // 6 + 1)
# EPSG = CONST + ZONE where CONST is
# - 32600 for positive latitudes
# - 32700 for negative latitudes
const = 32600 if lat > 0 else 32700
return const + zone
|
"""
**kwargs
"""
def print_info(**kwargs):
for key in kwargs:
print('{}: {}'.format(key, kwargs[key]))
if __name__ == '__main__':
print_info(name='Mike', lastname='Red', age=22) |
def clean_gdp(gdp):
# get needed columns from gdplev excel file
columns_to_keep = ['Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6']
gdp = gdp[columns_to_keep]
gdp.columns = ['Quarter', 'GDP Current', 'GDP Chained']
gdp = gdp[~gdp['Quarter'].isnull()]
# only keep data from 2000 onwards
gdp = gdp[gdp['Quarter'].str.startswith('2')]
gdp.reset_index(drop = True, inplace = True)
# create column to compare GDP change from quarter to quarter
gdp['GDP Change'] = gdp['GDP Current'] - gdp['GDP Current'].shift(1)
return gdp
def get_recession_start(gdp):
'''Returns the year and quarter of the recession start time as a
string value in a format such as 2005q3
'''
# look for two successive quarters with negative change in GDP
start_qtr = ''
for j in range(1,len(gdp)-1):
if gdp.iloc[j]['GDP Change']<0 and gdp.iloc[j-1]['GDP Change']<0:
start_qtr = gdp.iloc[j-2]['Quarter']
break
return start_qtr
def get_recession_end(gdp, rec_start):
'''Returns the year and quarter of the recession end time as a
string value in a format such as 2005q3
'''
# start at the beginning of the recession
rec_start_ix = gdp.Quarter[gdp.Quarter == rec_start].index.tolist()[0]
end_qtr = ''
# look for 2 successive quarters of increasing GDP
for j in range(rec_start_ix,len(gdp)-1):
if gdp.iloc[j]['GDP Change']>0 and gdp.iloc[j+1]['GDP Change']>0:
end_qtr = gdp.iloc[j+1]['Quarter']
break
return end_qtr
def get_recession_bottom(gdp, rec_start, rec_end):
'''Returns the year and quarter of the recession bottom time as a
string value in a format such as 2005q3
'''
# get index locations of recession start and end
rec_start_ix = gdp.Quarter[gdp.Quarter == rec_start].index.tolist()[0]
rec_end_ix = gdp.Quarter[gdp.Quarter == rec_end].index.tolist()[0]
gdp['GDP Current'] = gdp['GDP Current'].astype(float).fillna(0.0)
bottom_qtr = ''
lowest_gdp = gdp.iloc[rec_start_ix]['GDP Current']
# look for 2 successive quarters of increasing GDP
for j in range(rec_start_ix, rec_end_ix):
if gdp.iloc[j]['GDP Current'] < lowest_gdp:
bottom_qtr = gdp.iloc[j]['Quarter']
lowest_gdp = gdp.iloc[j]['GDP Current']
return bottom_qtr
|
# Can be used in the test data like ${MyObject()} or ${MyObject(1)}
class MyObject:
def __init__(self, index=''):
self.index = index
def __str__(self):
return '<MyObject%s>' % self.index
UNICODE = (u'Hyv\u00E4\u00E4 y\u00F6t\u00E4. '
u'\u0421\u043F\u0430\u0441\u0438\u0431\u043E!')
LIST_WITH_OBJECTS = [MyObject(1), MyObject(2)]
NESTED_LIST = [ [True, False], [[1, None, MyObject(), {}]] ]
NESTED_TUPLE = ( (True, False), [(1, None, MyObject(), {})] )
DICT_WITH_OBJECTS = {'As value': MyObject(1), MyObject(2): 'As key'}
NESTED_DICT = { 1: {None: False},
2: {'A': {'n': None},
'B': {'o': MyObject(), 'e': {}}} }
|
def Fibonacci(n):
# Check if input is 0 then it will
# print incorrect input
if n < 0:
print("Incorrect input")
# Check if n is 0
# then it will return 0
elif n == 0:
return 0
# Check if n is 1,2
# it will return 1
elif n == 1 or n == 2:
return 1
else:
return Fibonacci(n-1) + Fibonacci(n-2)
# Driver Program
print(Fibonacci(40)) |
# encoding: utf-8
# Copyright 2011 Tree.io Limited
# This file is part of Treeio.
# License www.tree.io/license
"""
Documents docstring
"""
|
class Solution(object):
def findPeakElementLinear(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
i = 0
while i < len(nums):
# check dangerous condition first
if i == len(nums) - 1 or (nums[i + 1] < nums[i]):
return i
i += 1
def findPeakElementBisec(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
l, r = 0, len(nums) - 1
if not nums:
return
if len(nums) == 1:
return 0
if len(nums) == 2:
return 0 if nums[0] > nums[1] else 1
while l < r: # should not exclude equal case
m = (l + r) // 2
if nums[m] > nums[m - 1] and nums[m] > nums[m + 1]:
return m
elif nums[m] < nums[m - 1]:
# climb left uphill
r = m - 1
elif nums[m] < nums[m + 1]:
# climb right uphill
l = m + 1
# no peak exists
return l if nums[0] > nums[-1] else r
|
for t in range(int(input())):
a,b = input().split()
cnt1,cnt2 = 0,0
for i in range(len(a)):
if a[i] != b[i]:
if b[i] == '0':
cnt1+=1
else:
cnt2+=1
print(max(cnt1,cnt2)) |
class Solution:
def subsets(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
def deep_subsets(nums):
r = []
n = len(nums)
if n == 1:
return [nums]
for idx, num in enumerate(nums):
r += [[num]]
for sb in deep_subsets(nums[idx+1:]):
r += [[num] + sb]
return r
r = [[]]
r += deep_subsets(nums)
return r
|
# @license Apache-2.0
#
# Copyright (c) 2018 The Stdlib Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# A `.gyp` file for building a Node.js native add-on.
#
# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
{
# List of files to include in this file:
'includes': [
'./include.gypi',
],
# Define variables to be used throughout the configuration for all targets:
'variables': {
# Target name should match the add-on export name:
'addon_target_name%': 'addon',
# Fortran compiler (to override -Dfortran_compiler=<compiler>):
'fortran_compiler%': 'gfortran',
# Fortran compiler flags:
'fflags': [
# Specify the Fortran standard to which a program is expected to conform:
'-std=f95',
# Indicate that the layout is free-form source code:
'-ffree-form',
# Aggressive optimization:
'-O3',
# Enable commonly used warning options:
'-Wall',
# Warn if source code contains problematic language features:
'-Wextra',
# Warn if a procedure is called without an explicit interface:
'-Wimplicit-interface',
# Do not transform names of entities specified in Fortran source files by appending underscores (i.e., don't mangle names, thus allowing easier usage in C wrappers):
'-fno-underscoring',
# Warn if source code contains Fortran 95 extensions and C-language constructs:
'-pedantic',
# Compile but do not link (output is an object file):
'-c',
],
# Set variables based on the host OS:
'conditions': [
[
'OS=="win"',
{
# Define the object file suffix:
'obj': 'obj',
},
{
# Define the object file suffix:
'obj': 'o',
}
], # end condition (OS=="win")
], # end conditions
}, # end variables
# Define compile targets:
'targets': [
# Target to generate an add-on:
{
# The target name should match the add-on export name:
'target_name': '<(addon_target_name)',
# Define dependencies:
'dependencies': [],
# Define directories which contain relevant include headers:
'include_dirs': [
# Local include directory:
'<@(include_dirs)',
],
# List of source files:
'sources': [
'<@(src_files)',
],
# Settings which should be applied when a target's object files are used as linker input:
'link_settings': {
# Define libraries:
'libraries': [
'<@(libraries)',
],
# Define library directories:
'library_dirs': [
'<@(library_dirs)',
],
},
# C/C++ compiler flags:
'cflags': [
# Enable commonly used warning options:
'-Wall',
# Aggressive optimization:
'-O3',
],
# C specific compiler flags:
'cflags_c': [
# Specify the C standard to which a program is expected to conform:
'-std=c99',
],
# C++ specific compiler flags:
'cflags_cpp': [
# Specify the C++ standard to which a program is expected to conform:
'-std=c++11',
],
# Linker flags:
'ldflags': [],
# Apply conditions based on the host OS:
'conditions': [
[
'OS=="mac"',
{
# Linker flags:
'ldflags': [
'-undefined dynamic_lookup',
'-Wl,-no-pie',
'-Wl,-search_paths_first',
],
},
], # end condition (OS=="mac")
[
'OS!="win"',
{
# C/C++ flags:
'cflags': [
# Generate platform-independent code:
'-fPIC',
],
},
], # end condition (OS!="win")
], # end conditions
# Define custom build actions for particular inputs:
'rules': [
{
# Define a rule for processing Fortran files:
'extension': 'f',
# Define the pathnames to be used as inputs when performing processing:
'inputs': [
# Full path of the current input:
'<(RULE_INPUT_PATH)'
],
# Define the outputs produced during processing:
'outputs': [
# Store an output object file in a directory for placing intermediate results (only accessible within a single target):
'<(INTERMEDIATE_DIR)/<(RULE_INPUT_ROOT).<(obj)'
],
# Define the rule for compiling Fortran based on the host OS:
'conditions': [
[
'OS=="win"',
# Rule to compile Fortran on Windows:
{
'rule_name': 'compile_fortran_windows',
'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Windows...',
'process_outputs_as_sources': 0,
# Define the command-line invocation:
'action': [
'<(fortran_compiler)',
'<@(fflags)',
'<@(_inputs)',
'-o',
'<@(_outputs)',
],
},
# Rule to compile Fortran on non-Windows:
{
'rule_name': 'compile_fortran_linux',
'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Linux...',
'process_outputs_as_sources': 1,
# Define the command-line invocation:
'action': [
'<(fortran_compiler)',
'<@(fflags)',
'-fPIC', # generate platform-independent code
'<@(_inputs)',
'-o',
'<@(_outputs)',
],
}
], # end condition (OS=="win")
], # end conditions
}, # end rule (extension=="f")
], # end rules
}, # end target <(addon_target_name)
# Target to copy a generated add-on to a standard location:
{
'target_name': 'copy_addon',
# Declare that the output of this target is not linked:
'type': 'none',
# Define dependencies:
'dependencies': [
# Require that the add-on be generated before building this target:
'<(addon_target_name)',
],
# Define a list of actions:
'actions': [
{
'action_name': 'copy_addon',
'message': 'Copying addon...',
# Explicitly list the inputs in the command-line invocation below:
'inputs': [],
# Declare the expected outputs:
'outputs': [
'<(addon_output_dir)/<(addon_target_name).node',
],
# Define the command-line invocation:
'action': [
'cp',
'<(PRODUCT_DIR)/<(addon_target_name).node',
'<(addon_output_dir)/<(addon_target_name).node',
],
},
], # end actions
}, # end target copy_addon
], # end targets
}
|
'''
In this module, we implement selection sort
Time complexity: O(n ^ 2)
'''
def selection_sort(arr):
'''
Sort array using selection sort
'''
for index_x in range(len(arr)):
min_index = index_x
for index_y in range(index_x + 1, len(arr)):
if arr[index_y] < arr[min_index]:
min_index = index_y
arr[min_index], arr[index_x] = arr[index_x], arr[min_index]
|
# Link : https://leetcode.com/problems/subtree-of-another-tree/
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def sameTree(self , root , subRoot):
if(root == None or subRoot == None):
return root == None and subRoot == None
# If one node matches , check if all its nodes match or not
elif(root.val == subRoot.val):
return self.sameTree(root.right , subRoot.right) and self.sameTree(root.left , subRoot.left)
else:
return False
def isSubtree(self, root, subRoot):
"""
:type root: TreeNode
:type subRoot: TreeNode
:rtype: bool
"""
# Base Cases
# If none
if(root == None):
return False
# If subtree and tree is same
elif(self.sameTree(root , subRoot)):
return True
else:
return self.isSubtree(root.right , subRoot) or self.isSubtree(root.left , subRoot)
|
"""Cabal packages"""
load("@bazel_skylib//lib:paths.bzl", "paths")
load("@bazel_tools//tools/build_defs/repo:utils.bzl", "maybe")
load("@bazel_tools//tools/cpp:toolchain_utils.bzl", "find_cpp_toolchain")
load(":cc.bzl", "cc_interop_info")
load(":private/context.bzl", "haskell_context", "render_env")
load(":private/dependencies.bzl", "gather_dep_info")
load(":private/expansions.bzl", "expand_make_variables")
load(":private/mode.bzl", "is_profiling_enabled")
load(":private/path_utils.bzl", "join_path_list", "truly_relativize")
load(":private/set.bzl", "set")
load(":haddock.bzl", "generate_unified_haddock_info")
load(
":private/workspace_utils.bzl",
_execute_or_fail_loudly = "execute_or_fail_loudly",
)
load(
":providers.bzl",
"HaddockInfo",
"HaskellInfo",
"HaskellLibraryInfo",
"get_ghci_extra_libs",
)
def _so_extension(hs):
return "dylib" if hs.toolchain.is_darwin else "so"
def _dirname(file):
return file.dirname
def _version(name):
"""Return the version component of a package name."""
return name.rpartition("-")[2]
def _has_version(name):
"""Check whether a package identifier has a version component."""
return name.rpartition("-")[2].replace(".", "").isdigit()
def _chop_version(name):
"""Remove any version component from the given package name."""
return name.rpartition("-")[0]
def _find_cabal(hs, srcs):
"""Check that a .cabal file exists. Choose the root one."""
cabal = None
for f in srcs:
if f.extension == "cabal":
if not cabal or f.dirname < cabal.dirname:
cabal = f
if not cabal:
fail("A .cabal file was not found in the srcs attribute.")
return cabal
def _find_setup(hs, cabal, srcs):
"""Check that a Setup script exists. If not, create a default one."""
setup = None
for f in srcs:
if f.basename in ["Setup.hs", "Setup.lhs"]:
if not setup or f.dirname < setup.dirname:
setup = f
if not setup:
setup = hs.actions.declare_file("Setup.hs", sibling = cabal)
hs.actions.write(
output = setup,
content = """
module Main where
import Distribution.Simple
main :: IO ()
main = defaultMain
""",
)
return setup
_CABAL_TOOLS = ["alex", "c2hs", "cpphs", "doctest", "happy"]
_CABAL_TOOL_LIBRARIES = ["cpphs", "doctest"]
# Some old packages are empty compatibility shims. Empty packages
# cause Cabal to not produce the outputs it normally produces. Instead
# of detecting that, we blacklist the offending packages, on the
# assumption that such packages are old and rare.
#
# TODO: replace this with a more general solution.
_EMPTY_PACKAGES_BLACKLIST = [
"bytestring-builder",
"fail",
"mtl-compat",
"nats",
]
def _cabal_tool_flag(tool):
"""Return a --with-PROG=PATH flag if input is a recognized Cabal tool. None otherwise."""
if tool.basename in _CABAL_TOOLS:
return "--with-{}={}".format(tool.basename, tool.path)
def _binary_paths(binaries):
return [binary.dirname for binary in binaries.to_list()]
def _prepare_cabal_inputs(hs, cc, posix, dep_info, cc_info, direct_cc_info, component, package_id, tool_inputs, tool_input_manifests, cabal, setup, srcs, compiler_flags, flags, cabal_wrapper, package_database):
"""Compute Cabal wrapper, arguments, inputs."""
with_profiling = is_profiling_enabled(hs)
# Haskell library dependencies or indirect C library dependencies are
# already covered by their corresponding package-db entries. We only need
# to add libraries and headers for direct C library dependencies to the
# command line.
(direct_libs, _) = get_ghci_extra_libs(hs, posix, direct_cc_info)
(transitive_libs, env) = get_ghci_extra_libs(hs, posix, cc_info)
env.update(**hs.env)
env["PATH"] = join_path_list(hs, _binary_paths(tool_inputs) + posix.paths)
if hs.toolchain.is_darwin:
env["SDKROOT"] = "macosx" # See haskell/private/actions/link.bzl
args = hs.actions.args()
package_databases = dep_info.package_databases
transitive_headers = cc_info.compilation_context.headers
direct_include_dirs = depset(transitive = [
direct_cc_info.compilation_context.includes,
direct_cc_info.compilation_context.quote_includes,
direct_cc_info.compilation_context.system_includes,
])
direct_lib_dirs = [file.dirname for file in direct_libs.to_list()]
args.add_all([component, package_id, setup, cabal.dirname, package_database.dirname])
args.add("--flags=" + " ".join(flags))
args.add_all(compiler_flags, format_each = "--ghc-option=%s")
args.add("--")
args.add_all(package_databases, map_each = _dirname, format_each = "--package-db=%s")
args.add_all(direct_include_dirs, format_each = "--extra-include-dirs=%s")
args.add_all(direct_lib_dirs, format_each = "--extra-lib-dirs=%s", uniquify = True)
if with_profiling:
args.add("--enable-profiling")
# Redundant with _binary_paths() above, but better be explicit when we can.
args.add_all(tool_inputs, map_each = _cabal_tool_flag)
inputs = depset(
[setup, hs.tools.ghc, hs.tools.ghc_pkg, hs.tools.runghc],
transitive = [
depset(srcs),
depset(cc.files),
package_databases,
transitive_headers,
transitive_libs,
dep_info.interface_dirs,
dep_info.static_libraries,
dep_info.dynamic_libraries,
tool_inputs,
],
)
input_manifests = tool_input_manifests
return struct(
cabal_wrapper = cabal_wrapper,
args = args,
inputs = inputs,
input_manifests = input_manifests,
env = env,
)
def _haskell_cabal_library_impl(ctx):
hs = haskell_context(ctx)
dep_info = gather_dep_info(ctx, ctx.attr.deps)
cc = cc_interop_info(ctx)
# All C and Haskell library dependencies.
cc_info = cc_common.merge_cc_infos(
cc_infos = [dep[CcInfo] for dep in ctx.attr.deps if CcInfo in dep],
)
# Separate direct C library dependencies.
direct_cc_info = cc_common.merge_cc_infos(
cc_infos = [
dep[CcInfo]
for dep in ctx.attr.deps
if CcInfo in dep and not HaskellInfo in dep
],
)
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
package_id = "{}-{}".format(
ctx.attr.package_name if ctx.attr.package_name else hs.label.name,
ctx.attr.version,
)
with_profiling = is_profiling_enabled(hs)
user_compile_flags = _expand_make_variables("compiler_flags", ctx, ctx.attr.compiler_flags)
cabal = _find_cabal(hs, ctx.files.srcs)
setup = _find_setup(hs, cabal, ctx.files.srcs)
package_database = hs.actions.declare_file(
"_install/{}.conf.d/package.cache".format(package_id),
sibling = cabal,
)
interfaces_dir = hs.actions.declare_directory(
"_install/{}_iface".format(package_id),
sibling = cabal,
)
data_dir = hs.actions.declare_directory(
"_install/{}_data".format(package_id),
sibling = cabal,
)
haddock_file = hs.actions.declare_file(
"_install/{}_haddock/{}.haddock".format(package_id, ctx.attr.name),
sibling = cabal,
)
haddock_html_dir = hs.actions.declare_directory(
"_install/{}_haddock_html".format(package_id),
sibling = cabal,
)
static_library_filename = "_install/lib/libHS{}.a".format(package_id)
if with_profiling:
static_library_filename = "_install/lib/libHS{}_p.a".format(package_id)
static_library = hs.actions.declare_file(
static_library_filename,
sibling = cabal,
)
if hs.toolchain.is_static:
dynamic_library = None
else:
dynamic_library = hs.actions.declare_file(
"_install/lib/libHS{}-ghc{}.{}".format(
package_id,
hs.toolchain.version,
_so_extension(hs),
),
sibling = cabal,
)
(tool_inputs, tool_input_manifests) = ctx.resolve_tools(tools = ctx.attr.tools)
c = _prepare_cabal_inputs(
hs,
cc,
posix,
dep_info,
cc_info,
direct_cc_info,
component = "lib:{}".format(
ctx.attr.package_name if ctx.attr.package_name else hs.label.name,
),
package_id = package_id,
tool_inputs = tool_inputs,
tool_input_manifests = tool_input_manifests,
cabal = cabal,
setup = setup,
srcs = ctx.files.srcs,
compiler_flags = user_compile_flags,
flags = ctx.attr.flags,
cabal_wrapper = ctx.executable._cabal_wrapper,
package_database = package_database,
)
ctx.actions.run(
executable = c.cabal_wrapper,
arguments = [c.args],
inputs = c.inputs,
input_manifests = c.input_manifests,
tools = [c.cabal_wrapper],
outputs = [
package_database,
interfaces_dir,
static_library,
data_dir,
haddock_file,
haddock_html_dir,
] + ([dynamic_library] if dynamic_library != None else []),
env = c.env,
mnemonic = "HaskellCabalLibrary",
progress_message = "HaskellCabalLibrary {}".format(hs.label),
)
default_info = DefaultInfo(
files = depset([static_library] + ([dynamic_library] if dynamic_library != None else [])),
runfiles = ctx.runfiles(
files = [data_dir],
collect_default = True,
),
)
hs_info = HaskellInfo(
package_databases = depset([package_database], transitive = [dep_info.package_databases]),
version_macros = set.empty(),
source_files = depset(),
extra_source_files = depset(),
import_dirs = set.empty(),
static_libraries = depset(
direct = [static_library],
transitive = [dep_info.static_libraries],
order = "topological",
),
dynamic_libraries = depset(
direct = [dynamic_library] if dynamic_library != None else [],
transitive = [dep_info.dynamic_libraries],
),
interface_dirs = depset([interfaces_dir], transitive = [dep_info.interface_dirs]),
compile_flags = [],
)
lib_info = HaskellLibraryInfo(package_id = package_id, version = None, exports = [])
doc_info = generate_unified_haddock_info(
this_package_id = package_id,
this_package_html = haddock_html_dir,
this_package_haddock = haddock_file,
deps = ctx.attr.deps,
)
cc_toolchain = find_cpp_toolchain(ctx)
feature_configuration = cc_common.configure_features(
ctx = ctx,
cc_toolchain = cc_toolchain,
requested_features = ctx.features,
unsupported_features = ctx.disabled_features,
)
library_to_link = cc_common.create_library_to_link(
actions = ctx.actions,
feature_configuration = feature_configuration,
dynamic_library = dynamic_library,
static_library = static_library,
cc_toolchain = cc_toolchain,
)
compilation_context = cc_common.create_compilation_context()
linking_context = cc_common.create_linking_context(
libraries_to_link = [library_to_link],
)
cc_info = cc_common.merge_cc_infos(
cc_infos = [
CcInfo(
compilation_context = compilation_context,
linking_context = linking_context,
),
cc_info,
],
)
return [default_info, hs_info, cc_info, lib_info, doc_info]
haskell_cabal_library = rule(
_haskell_cabal_library_impl,
attrs = {
"package_name": attr.string(
doc = "Cabal package name. Defaults to name attribute.",
),
"version": attr.string(
doc = "Version of the Cabal package.",
mandatory = True,
),
"srcs": attr.label_list(allow_files = True),
"deps": attr.label_list(),
"compiler_flags": attr.string_list(
doc = """Flags to pass to Haskell compiler, in addition to those defined
the cabal file. Subject to Make variable substitution.""",
),
"tools": attr.label_list(
cfg = "host",
allow_files = True,
doc = """Tool dependencies. They are built using the host configuration, since
the tools are executed as part of the build.""",
),
"flags": attr.string_list(
doc = "List of Cabal flags, will be passed to `Setup.hs configure --flags=...`.",
),
"_cabal_wrapper": attr.label(
executable = True,
cfg = "host",
default = Label("@rules_haskell//haskell:cabal_wrapper"),
),
"_cc_toolchain": attr.label(
default = Label("@bazel_tools//tools/cpp:current_cc_toolchain"),
),
},
toolchains = [
"@bazel_tools//tools/cpp:toolchain_type",
"@rules_haskell//haskell:toolchain",
"@rules_sh//sh/posix:toolchain_type",
],
fragments = ["cpp"],
)
"""Use Cabal to build a library.
Example:
```bzl
haskell_cabal_library(
name = "lib-0.1.0.0",
srcs = ["lib.cabal", "Lib.hs", "Setup.hs"],
)
haskell_toolchain_library(name = "base")
haskell_binary(
name = "bin",
deps = [":base", ":lib-0.1.0.0"],
srcs = ["Main.hs"],
)
```
This rule does not use `cabal-install`. It calls the package's
`Setup.hs` script directly if one exists, or the default one if not.
All sources files that would have been part of a Cabal sdist need to
be listed in `srcs` (crucially, including the `.cabal` file).
A `haskell_cabal_library` can be substituted for any
`haskell_library`. The two are interchangeable in most contexts.
However, using a plain `haskell_library` sometimes leads to better
build times, and does not require drafting a `.cabal` file.
"""
def _haskell_cabal_binary_impl(ctx):
hs = haskell_context(ctx)
dep_info = gather_dep_info(ctx, ctx.attr.deps)
cc = cc_interop_info(ctx)
# All C and Haskell library dependencies.
cc_info = cc_common.merge_cc_infos(
cc_infos = [dep[CcInfo] for dep in ctx.attr.deps if CcInfo in dep],
)
# Separate direct C library dependencies.
direct_cc_info = cc_common.merge_cc_infos(
cc_infos = [
dep[CcInfo]
for dep in ctx.attr.deps
if CcInfo in dep and not HaskellInfo in dep
],
)
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
user_compile_flags = _expand_make_variables("compiler_flags", ctx, ctx.attr.compiler_flags)
cabal = _find_cabal(hs, ctx.files.srcs)
setup = _find_setup(hs, cabal, ctx.files.srcs)
package_database = hs.actions.declare_file(
"_install/{}.conf.d/package.cache".format(hs.label.name),
sibling = cabal,
)
binary = hs.actions.declare_file(
"_install/bin/{name}{ext}".format(
name = hs.label.name,
ext = ".exe" if hs.toolchain.is_windows else "",
),
sibling = cabal,
)
data_dir = hs.actions.declare_directory(
"_install/{}_data".format(hs.label.name),
sibling = cabal,
)
(tool_inputs, tool_input_manifests) = ctx.resolve_tools(tools = ctx.attr.tools)
c = _prepare_cabal_inputs(
hs,
cc,
posix,
dep_info,
cc_info,
direct_cc_info,
component = "exe:{}".format(hs.label.name),
package_id = hs.label.name,
tool_inputs = tool_inputs,
tool_input_manifests = tool_input_manifests,
cabal = cabal,
setup = setup,
srcs = ctx.files.srcs,
compiler_flags = user_compile_flags,
flags = ctx.attr.flags,
cabal_wrapper = ctx.executable._cabal_wrapper,
package_database = package_database,
)
ctx.actions.run(
executable = c.cabal_wrapper,
arguments = [c.args],
inputs = c.inputs,
input_manifests = c.input_manifests,
outputs = [
package_database,
binary,
data_dir,
],
tools = [c.cabal_wrapper],
env = c.env,
mnemonic = "HaskellCabalBinary",
progress_message = "HaskellCabalBinary {}".format(hs.label),
)
hs_info = HaskellInfo(
package_databases = dep_info.package_databases,
version_macros = set.empty(),
source_files = depset(),
extra_source_files = depset(),
import_dirs = set.empty(),
static_libraries = dep_info.static_libraries,
dynamic_libraries = dep_info.dynamic_libraries,
interface_dirs = dep_info.interface_dirs,
compile_flags = [],
)
default_info = DefaultInfo(
files = depset([binary]),
executable = binary,
runfiles = ctx.runfiles(
files = [data_dir],
collect_default = True,
),
)
return [hs_info, cc_info, default_info]
haskell_cabal_binary = rule(
_haskell_cabal_binary_impl,
executable = True,
attrs = {
"srcs": attr.label_list(allow_files = True),
"deps": attr.label_list(),
"compiler_flags": attr.string_list(
doc = """Flags to pass to Haskell compiler, in addition to those defined
the cabal file. Subject to Make variable substitution.""",
),
"tools": attr.label_list(
cfg = "host",
doc = """Tool dependencies. They are built using the host configuration, since
the tools are executed as part of the build.""",
),
"flags": attr.string_list(
doc = "List of Cabal flags, will be passed to `Setup.hs configure --flags=...`.",
),
"_cabal_wrapper": attr.label(
executable = True,
cfg = "host",
default = Label("@rules_haskell//haskell:cabal_wrapper"),
),
"_cc_toolchain": attr.label(
default = Label("@bazel_tools//tools/cpp:current_cc_toolchain"),
),
},
toolchains = [
"@bazel_tools//tools/cpp:toolchain_type",
"@rules_haskell//haskell:toolchain",
"@rules_sh//sh/posix:toolchain_type",
],
fragments = ["cpp"],
)
"""Use Cabal to build a binary.
Example:
```bzl
haskell_cabal_binary(
name = "happy",
srcs = glob(["**"]),
)
```
This rule assumes that the .cabal file defines a single executable
with the same name as the package.
This rule does not use `cabal-install`. It calls the package's
`Setup.hs` script directly if one exists, or the default one if not.
All sources files that would have been part of a Cabal sdist need to
be listed in `srcs` (crucially, including the `.cabal` file).
"""
# Temporary hardcoded list of core libraries. This will no longer be
# necessary once Stack 2.0 is released.
#
# TODO remove this list and replace it with Stack's --global-hints
# mechanism.
_CORE_PACKAGES = [
"Cabal",
"array",
"base",
"binary",
"bytestring",
"containers",
"deepseq",
"directory",
"filepath",
"ghc",
"ghc-boot",
"ghc-boot-th",
"ghc-compact",
"ghc-heap",
"ghc-prim",
"ghci",
"haskeline",
"hpc",
"integer-gmp",
"integer-simple",
"libiserv",
"mtl",
"parsec",
"pretty",
"process",
"rts",
"stm",
"template-haskell",
"terminfo",
"text",
"time",
"transformers",
"unix",
"Win32",
"xhtml",
]
_STACK_DEFAULT_VERSION = "2.1.3"
# Only ever need one version, but use same structure as for GHC bindists.
_STACK_BINDISTS = \
{
"2.1.3": {
"freebsd-x86_64": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-freebsd-x86_64.tar.gz",
"b646380bd1ee6c5f16ea111c31be494e6e85ed5050dea41cd29fac5973767821",
),
"linux-aarch64": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-linux-aarch64.tar.gz",
"1212c3ef9c4e901c50b086f1d778c28d75eb27cb4529695d2f1a16ea3f898a6d",
),
"linux-arm": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-linux-arm.tar.gz",
"6c8a2100183368d0fe8298bc99260681f10c81838423884be885baaa2e096e78",
),
"linux-i386": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-linux-i386.tar.gz",
"4acd97f4c91b1d1333c8d84ea38f690f0b5ac5224ba591f8cdd1b9d0e8973807",
),
"linux-x86_64": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-linux-x86_64.tar.gz",
"c724b207831fe5f06b087bac7e01d33e61a1c9cad6be0468f9c117d383ec5673",
),
"osx-x86_64": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-osx-x86_64.tar.gz",
"84b05b9cdb280fbc4b3d5fe23d1fc82a468956c917e16af7eeeabec5e5815d9f",
),
"windows-i386": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-windows-i386.tar.gz",
"9bc67a8dc0466b6fc12b44b3920ea6be3b00fa1c52cbeada1a7c092a5402ebb3",
),
"windows-x86_64": (
"https://github.com/commercialhaskell/stack/releases/download/v2.1.3/stack-2.1.3-windows-x86_64.tar.gz",
"075bcd9130cd437de4e726466e5738c92c8e47d5666aa3a15d339e6ba62f76b2",
),
},
}
def _stack_version_check(repository_ctx, stack_cmd):
"""Returns False if version not recent enough."""
exec_result = _execute_or_fail_loudly(repository_ctx, [stack_cmd, "--numeric-version"])
stack_major_version = int(exec_result.stdout.split(".")[0])
return stack_major_version >= 2
def _compute_dependency_graph(repository_ctx, snapshot, core_packages, versioned_packages, unversioned_packages, vendored_packages):
"""Given a list of root packages, compute a dependency graph.
Returns:
dict(name: struct(name, version, versioned_name, deps, is_core_package, sdist)):
name: The unversioned package name.
version: The version of the package.
versioned_name: <name>-<version>.
flags: Cabal flags for this package.
deps: The list of dependencies.
vendored: Label of vendored package, None if not vendored.
is_core_package: Whether the package is a core package.
sdist: directory name of the unpackaged source distribution or None if core package or vendored.
"""
all_packages = {}
for core_package in core_packages:
all_packages[core_package] = struct(
name = core_package,
version = None,
versioned_name = None,
flags = repository_ctx.attr.flags.get(core_package, []),
deps = [],
vendored = None,
is_core_package = True,
sdist = None,
)
if not versioned_packages and not unversioned_packages and not vendored_packages:
return all_packages
# Unpack all given packages, then compute the transitive closure
# and unpack anything in the transitive closure as well.
stack_cmd = repository_ctx.path(repository_ctx.attr.stack)
if not _stack_version_check(repository_ctx, stack_cmd):
fail("Stack version not recent enough. Need version 2.1 or newer.")
stack = [stack_cmd]
if versioned_packages:
_execute_or_fail_loudly(repository_ctx, stack + ["unpack"] + versioned_packages)
stack = [stack_cmd, "--resolver", snapshot]
if unversioned_packages:
_execute_or_fail_loudly(repository_ctx, stack + ["unpack"] + unversioned_packages)
exec_result = _execute_or_fail_loudly(repository_ctx, ["ls"])
unpacked_sdists = exec_result.stdout.splitlines()
# Determines path to vendored package's root directory relative to stack.yaml.
# Note, this requires that the Cabal file exists in the package root and is
# called `<name>.cabal`.
vendored_sdists = [
truly_relativize(
str(repository_ctx.path(label.relative(name + ".cabal")).dirname),
relative_to = str(repository_ctx.path("stack.yaml").dirname),
)
for (name, label) in vendored_packages.items()
]
package_flags = {
pkg_name: {
flag[1:] if flag.startswith("-") else flag: not flag.startswith("-")
for flag in flags
}
for (pkg_name, flags) in repository_ctx.attr.flags.items()
}
stack_yaml_content = struct(resolver = "none", packages = unpacked_sdists + vendored_sdists, flags = package_flags).to_json()
repository_ctx.file("stack.yaml", content = stack_yaml_content, executable = False)
exec_result = _execute_or_fail_loudly(
repository_ctx,
stack + ["ls", "dependencies", "--global-hints", "--separator=-"],
)
transitive_unpacked_sdists = []
indirect_unpacked_sdists = []
for package in exec_result.stdout.splitlines():
name = _chop_version(package)
if name in _CABAL_TOOLS and not name in _CABAL_TOOL_LIBRARIES:
continue
version = _version(package)
vendored = vendored_packages.get(name, None)
is_core_package = name in _CORE_PACKAGES
all_packages[name] = struct(
name = name,
version = version,
versioned_name = package,
flags = repository_ctx.attr.flags.get(name, []),
deps = [],
vendored = vendored,
is_core_package = is_core_package,
sdist = None if is_core_package or vendored != None else package,
)
if is_core_package or vendored != None:
continue
if version == "<unknown>":
fail("""\
Could not resolve version of {}. It is not in the snapshot.
Specify a fully qualified package name of the form <package>-<version>.
""".format(package))
transitive_unpacked_sdists.append(package)
if package not in unpacked_sdists:
indirect_unpacked_sdists.append(name)
# We removed the version numbers prior to calling `unpack`. This
# way, stack will fetch the package sources from the snapshot
# rather than from Hackage. See #1027.
if indirect_unpacked_sdists:
_execute_or_fail_loudly(repository_ctx, stack + ["unpack"] + indirect_unpacked_sdists)
stack_yaml_content = struct(resolver = "none", packages = transitive_unpacked_sdists + vendored_sdists, flags = package_flags).to_json()
repository_ctx.file("stack.yaml", stack_yaml_content, executable = False)
# Compute dependency graph.
exec_result = _execute_or_fail_loudly(
repository_ctx,
stack + ["dot", "--global-hints", "--external"],
)
for line in exec_result.stdout.splitlines():
tokens = [w.strip('";') for w in line.split(" ")]
# All lines of the form `"foo" -> "bar";` declare edges of the
# dependency graph in the Graphviz format.
if len(tokens) == 3 and tokens[1] == "->":
[src, _, dest] = tokens
if src in all_packages and dest in all_packages:
all_packages[src].deps.append(dest)
return all_packages
def _invert(d):
"""Invert a dictionary."""
return dict(zip(d.values(), d.keys()))
def _from_string_keyed_label_list_dict(d):
"""Convert string_keyed_label_list_dict to label_keyed_string_dict."""
# TODO Remove _from_string_keyed_label_list_dict once following issue
# is resolved: https://github.com/bazelbuild/bazel/issues/7989.
out = {}
for (string_key, label_list) in d.items():
for label in label_list:
if label in out:
out[label] += " " + string_key
else:
out[label] = string_key
return out
def _to_string_keyed_label_list_dict(d):
"""Convert label_keyed_string_dict to string_keyed_label_list_dict."""
# TODO Remove _to_string_keyed_label_list_dict once following issue
# is resolved: https://github.com/bazelbuild/bazel/issues/7989.
out = {}
for (label, string_key_list) in d.items():
for string_key in string_key_list.split(" "):
out.setdefault(string_key, []).append(label)
return out
def _label_to_string(label):
return "@{}//{}:{}".format(label.workspace_name, label.package, label.name)
def _stack_snapshot_impl(repository_ctx):
if repository_ctx.attr.snapshot and repository_ctx.attr.local_snapshot:
fail("Please specify either snapshot or local_snapshot, but not both.")
elif repository_ctx.attr.snapshot:
snapshot = repository_ctx.attr.snapshot
elif repository_ctx.attr.local_snapshot:
snapshot = repository_ctx.path(repository_ctx.attr.local_snapshot)
else:
fail("Please specify one of snapshot or repository_snapshot")
vendored_packages = _invert(repository_ctx.attr.vendored_packages)
packages = repository_ctx.attr.packages
core_packages = []
versioned_packages = []
unversioned_packages = []
for package in packages:
has_version = _has_version(package)
unversioned = _chop_version(package) if has_version else package
if unversioned in vendored_packages:
fail("Duplicate package '{}'. Packages may not be listed in both 'packages' and 'vendored_packages'.".format(package))
if unversioned in _CORE_PACKAGES:
core_packages.append(unversioned)
elif has_version:
versioned_packages.append(package)
else:
unversioned_packages.append(package)
all_packages = _compute_dependency_graph(
repository_ctx,
snapshot,
core_packages,
versioned_packages,
unversioned_packages,
vendored_packages,
)
extra_deps = _to_string_keyed_label_list_dict(repository_ctx.attr.extra_deps)
tools = [_label_to_string(label) for label in repository_ctx.attr.tools]
# Write out dependency graph as importable Starlark value.
repository_ctx.file(
"packages.bzl",
"packages = " + repr({
package.name: struct(
name = package.name,
version = package.version,
deps = [Label("@{}//:{}".format(repository_ctx.name, dep)) for dep in package.deps],
flags = package.flags,
)
for package in all_packages.values()
}),
executable = False,
)
# Write out the dependency graph as a BUILD file.
build_file_builder = []
build_file_builder.append("""
load("@rules_haskell//haskell:cabal.bzl", "haskell_cabal_library")
load("@rules_haskell//haskell:defs.bzl", "haskell_library", "haskell_toolchain_library")
""")
for package in all_packages.values():
if package.name in packages or package.versioned_name in packages or package.vendored != None:
visibility = ["//visibility:public"]
else:
visibility = ["//visibility:private"]
if package.vendored != None:
build_file_builder.append(
"""
alias(name = "{name}", actual = "{actual}", visibility = {visibility})
""".format(name = package.name, actual = package.vendored, visibility = visibility),
)
elif package.is_core_package:
build_file_builder.append(
"""
haskell_toolchain_library(name = "{name}", visibility = {visibility})
""".format(name = package.name, visibility = visibility),
)
elif package.name in _EMPTY_PACKAGES_BLACKLIST:
build_file_builder.append(
"""
haskell_library(
name = "{name}",
version = "{version}",
visibility = {visibility},
)
""".format(
name = package.name,
version = package.version,
visibility = visibility,
),
)
else:
build_file_builder.append(
"""
haskell_cabal_library(
name = "{name}",
version = "{version}",
flags = {flags},
srcs = glob(["{dir}/**"]),
deps = {deps},
tools = {tools},
visibility = {visibility},
compiler_flags = ["-w", "-optF=-w"],
)
""".format(
name = package.name,
version = package.version,
flags = package.flags,
dir = package.sdist,
deps = package.deps + [
_label_to_string(label)
for label in extra_deps.get(package.name, [])
],
tools = tools,
visibility = visibility,
),
)
if package.versioned_name != None:
build_file_builder.append(
"""alias(name = "{name}", actual = ":{actual}", visibility = {visibility})""".format(
name = package.versioned_name,
actual = package.name,
visibility = visibility,
),
)
build_file_content = "\n".join(build_file_builder)
repository_ctx.file("BUILD.bazel", build_file_content, executable = False)
_stack_snapshot = repository_rule(
_stack_snapshot_impl,
attrs = {
"snapshot": attr.string(),
"local_snapshot": attr.label(allow_single_file = True),
"packages": attr.string_list(),
"vendored_packages": attr.label_keyed_string_dict(),
"flags": attr.string_list_dict(),
"extra_deps": attr.label_keyed_string_dict(),
"tools": attr.label_list(),
"stack": attr.label(),
"stack_update": attr.label(),
},
)
def _stack_update_impl(repository_ctx):
stack_cmd = repository_ctx.path(repository_ctx.attr.stack)
_execute_or_fail_loudly(repository_ctx, [stack_cmd, "update"])
repository_ctx.file("stack_update")
repository_ctx.file("BUILD.bazel", content = "exports_files(['stack_update'])")
_stack_update = repository_rule(
_stack_update_impl,
attrs = {
"stack": attr.label(),
},
# Marked as local so that stack update is always executed before
# _stack_snapshot is executed.
local = True,
)
"""Execute stack update.
This is extracted into a singleton repository rule to avoid concurrent
invocations of stack update.
See https://github.com/tweag/rules_haskell/issues/1090
"""
def _get_platform(repository_ctx):
"""Map OS name and architecture to Stack platform identifiers."""
os_name = repository_ctx.os.name.lower()
if os_name.startswith("linux"):
os = "linux"
elif os_name.startswith("mac os"):
os = "osx"
elif os_name.find("freebsd") != -1:
os = "freebsd"
elif os_name.find("windows") != -1:
os = "windows"
else:
fail("Unknown OS: '{}'".format(os_name))
if os == "windows":
reg_query = ["reg", "QUERY", "HKLM\\SYSTEM\\CurrentControlSet\\Control\\Session Manager\\Environment", "/v", "PROCESSOR_ARCHITECTURE"]
result = repository_ctx.execute(reg_query)
value = result.stdout.strip().split(" ")[-1].lower()
if value in ["amd64", "ia64"]:
arch = "x86_64"
elif value in ["x86"]:
arch = "i386"
else:
fail("Failed to determine CPU architecture:\n{}\n{}".format(result.stdout, result.stderr))
else:
result = repository_ctx.execute(["uname", "-m"])
if result.stdout.strip() in ["arm", "armv7l"]:
arch = "arm"
elif result.stdout.strip() in ["aarch64"]:
arch = "aarch64"
elif result.stdout.strip() in ["amd64", "x86_64", "x64"]:
arch = "x86_64"
elif result.stdout.strip() in ["i386", "i486", "i586", "i686"]:
arch = "i386"
else:
fail("Failed to determine CPU architecture:\n{}\n{}".format(result.stdout, result.stderr))
return (os, arch)
def _fetch_stack_impl(repository_ctx):
repository_ctx.file("BUILD.bazel")
stack_cmd = repository_ctx.which("stack")
if stack_cmd:
if _stack_version_check(repository_ctx, stack_cmd):
repository_ctx.symlink(stack_cmd, "stack")
return
else:
print("Stack version not recent enough. Downloading a newer version...")
# If we can't find Stack, download it.
(os, arch) = _get_platform(repository_ctx)
version = _STACK_DEFAULT_VERSION
(url, sha256) = _STACK_BINDISTS[version]["{}-{}".format(os, arch)]
repository_ctx.download_and_extract(url = url, sha256 = sha256)
stack_cmd = repository_ctx.path(
"stack-{}-{}-{}".format(version, os, arch),
).get_child("stack.exe" if os == "windows" else "stack")
_execute_or_fail_loudly(repository_ctx, [stack_cmd, "--version"])
exec_result = repository_ctx.execute([stack_cmd, "--version"], quiet = True)
if exec_result.return_code != 0:
error_messsage = ["A Stack binary for your platform exists, but it failed to execute."]
if os == "linux":
error_messsage.append("HINT: If you are on NixOS,")
error_messsage.append("* make Stack available on the PATH, or")
error_messsage.append("* specify a Stack binary using the stack attribute.")
fail("\n".join(error_messsage).format(exec_result.return_code))
repository_ctx.symlink(stack_cmd, "stack")
_fetch_stack = repository_rule(
_fetch_stack_impl,
)
"""Find a suitably recent local Stack or download it."""
def stack_snapshot(stack = None, extra_deps = {}, vendored_packages = {}, **kwargs):
"""Use Stack to download and extract Cabal source distributions.
Args:
snapshot: The name of a Stackage snapshot. Incompatible with local_snapshot.
local_snapshot: A custom Stack snapshot file, as per the Stack documentation.
Incompatible with snapshot.
packages: A set of package identifiers. For packages in the snapshot,
version numbers can be omitted.
vendored_packages: Add or override a package to the snapshot with a custom
unpacked source distribution. Each package must contain a Cabal file
named `<package-name>.cabal` in the package root.
flags: A dict from package name to list of flags.
extra_deps: Extra dependencies of packages, e.g. system libraries or C/C++ libraries.
tools: Tool dependencies. They are built using the host configuration, since
the tools are executed as part of the build.
stack: The stack binary to use to enumerate package dependencies.
Examples:
```bzl
stack_snapshot(
name = "stackage",
packages = ["conduit", "lens", "zlib-0.6.2"],
vendored_packages = {"split": "//split:split"},
tools = ["@happy//:happy", "@c2hs//:c2hs"],
snapshot = "lts-13.15",
extra_deps = {"zlib": ["@zlib.dev//:zlib"]},
)
```
defines `@stackage//:conduit`, `@stackage//:lens`,
`@stackage//:zlib` library targets.
Alternatively
```bzl
stack_snapshot(
name = "stackage",
packages = ["conduit", "lens", "zlib"],
flags = {"zlib": ["-non-blocking-ffi"]},
tools = ["@happy//:happy", "@c2hs//:c2hs"],
local_Snapshot = "//:snapshot.yaml",
extra_deps = {"zlib": ["@zlib.dev//:zlib"]},
```
Does the same as the previous example, provided there is a
`snapshot.yaml`, at the root of the repository with content
```yaml
resolver: lts-13.15
packages:
- zlib-0.6.2
```
This rule will use Stack to compute the transitive closure of the
subset of the given snapshot listed in the `packages` attribute, and
generate a dependency graph. If a package in the closure depends on
system libraries or other external libraries, use the `extra_deps`
attribute to list them. This attribute works like the
`--extra-{include,lib}-dirs` flags for Stack and cabal-install do.
Packages that are in the snapshot need not have their versions
specified. But any additional packages or version overrides will have
to be specified with a package identifier of the form
`<package>-<version>` in the `packages` attribute.
In the external repository defined by the rule, all given packages are
available as top-level targets named after each package. Additionally, the
dependency graph is made available within `packages.bzl` as the `dict`
`packages` mapping unversioned package names to structs holding the fields
- name: The unversioned package name.
- version: The package version.
- deps: The list of package dependencies according to stack.
- flags: The list of Cabal flags.
"""
if not stack:
_fetch_stack(name = "rules_haskell_stack")
stack = Label("@rules_haskell_stack//:stack")
# Execute stack update once before executing _stack_snapshot.
# This is to avoid multiple concurrent executions of stack update,
# which may fail due to ~/.stack/pantry/hackage/hackage-security-lock.
# See https://github.com/tweag/rules_haskell/issues/1090.
maybe(
_stack_update,
name = "rules_haskell_stack_update",
stack = stack,
)
_stack_snapshot(
stack = stack,
# Dependency for ordered execution, stack update before stack unpack.
stack_update = "@rules_haskell_stack_update//:stack_update",
# TODO Remove _from_string_keyed_label_list_dict once following issue
# is resolved: https://github.com/bazelbuild/bazel/issues/7989.
extra_deps = _from_string_keyed_label_list_dict(extra_deps),
# TODO Remove _invert once following issue is resolved:
# https://github.com/bazelbuild/bazel/issues/7989.
vendored_packages = _invert(vendored_packages),
**kwargs
)
def _expand_make_variables(name, ctx, strings):
extra_label_attrs = [
ctx.attr.srcs,
ctx.attr.tools,
]
return expand_make_variables(name, ctx, strings, extra_label_attrs)
|
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